Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.2. Feature attributes > 3.2.2. Location properties
Type: text

Location is another significant feature that influences the severity of calamities caused by GLOF. The glacial lakes are generally located in uninhabited and remote mountain valleys, however, wide-ranging GLOFs can cause significant damage to properties as well as multiple casualties downstream up to tens of kilometres. Here, various location attributes, such as longitude, latitude, and altitude are computed from the input data, as the location of the glacial lake can be accurately found using these attributes.

- (i) *Longitude*: Longitude is a reference with respect to the prime meridian and it refers to the angular distance (decimal degree) to the west or east of the prime meridian. The longitude feature is represented as  $J_1$ .
- (ii) Latitude: Latitude gives the location of any place with respect to the equator, and is given as a measure of angular distance (decimal degree) to the south or north of the equator. Most of the glaciers are located in higher-latitude regions, such as the Arctic and Antarctic. But the low-latitude regions with high mountain ranges, like the Himalayas and Andes also contain glaciers. The latitude of the glacial lake measured is indicated as  $J_2$ .
- (iii) Altitude: This attribute refers to the elevation of the glacier lake above sea level and is measured in metres/feet. Glacial lakes are more commonly found in high-altitude regions, and their numbers have increased with the retreat of glaciers. Let  $J_3$  designate the altitude feature.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.2. Feature attributes > 3.2.3. Lake-based properties
Type: text

The devastating power of GLOF depends on the characteristics of the glacial lake and hence, these parameters

data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.2. Feature attributes > 3.2.3. Lake-based properties
Type: figure
Figure

Image /page/5/Figure/3 description: A flowchart titled 'Figure 1. Structural view of the proposed ShCNNFDM N for GLOF risk assessment.' The diagram illustrates a process for risk assessment. At the top, under 'Data and feature attributes', four categories of properties are listed with their corresponding data points: 'Geometric properties' lead to 'Shape of the lake'; 'Location properties' lead to 'Latitude, longitude, and altitude'; 'Lake-based properties' lead to 'Lake area, perimeter, water volume, lake type, and uncertainty'; and 'Global properties' lead to 'UTM grid zone and GTN-G region'. An arrow points down from 'Data and feature attributes' to two parallel processes: 'Risk assessment' on the left and 'Hazard assessment' on the right. The 'Hazard assessment' box contains a 'Proposed ShCNNFDM N' model, which is composed of 'Shepard Convolutional Neural Networks (ShCNN)' and 'Deep Maxout Network (DMN)'. The output of this assessment is a 'Hazard class'. The 'Risk assessment' box also contains a 'Proposed ShCNNFDM N' model, which receives inputs from 'ShCNN' and 'DMN'. This risk assessment process also takes the 'Hazard class' as an input. The final output from the 'Risk assessment' box is the 'Risk class'.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.2. Feature attributes > 3.2.3. Lake-based properties
Type: figure
Figure: Figure 1. Structural view of the proposed ShCNNFDMN for GLOF risk assessment.

Figure 1. Structural view of the proposed ShCNNFDMN for GLOF risk assessment.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.2. Feature attributes > 3.2.3. Lake-based properties
Type: text

have to be considered while evaluating the risk assessment. The lake-based properties, such as lake area, perimeter, water volume, lake type, and uncertainty (Zheng et al. 2021) are considered here.

- (i) Lake area: Lake area indicates the surface area of the lake and is obtained by multiplying the length and width of the lake and is expressed as  $J_4$ . Lake area is measured in square metres and is measured based on the UTM.
- (ii) *Perimeter*: The total length of the shoreline line of the lake gives the perimeter value and is indicated
- (iii) Water volume: The volume of the lake is measured by finding the product of the surface area of the

- lake, and is expressed in cubic metres. The water volume feature of the glacial lake is characterised as  $J_6$ . The destructive potential of GLOF depends on the volume of water contained in the lake.
- (iv) Lake type: The impact of GLOF depends on the type of the glacial lakes, which can be either Ice (I), Moraine (M), or others (O). Moraine-dammed glacial lakes are found to be a more prominent cause of GLOF than other glacial lakes. The laketype feature is symbolised as  $J_7$ .
- (v) Uncertainty: This parameter deals with the uncertainty of the measurements of the glacial topology obtained using remote sensing techniques, and is considered so that the values can be analyzed as per their relevance. Uncertainty of the lake area is

jan

expressed as,

$$J_8 = J_6/A \times A^2/2 \times 0.6872 \tag{2}$$

where A refers to the image's spatial resolution, I6 designates the perimeter, and  $J_8$  represents the uncertainty feature.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.2. Feature attributes > 3.2.4. Global properties
Type: text

In addition to the above features, the global properties, such as the UTM grid zone, and GTN-G region of the glacial lake are considered to improve the risk assessment efficiency.

- (i) UTM grid zone: This refers to the UTM zone in which the glacial lake is present. The UTM coordinate system partitions the globe into 60 zones, and in all zones, the coordinates are measured in northings and eastings in metres, and each zone has a width of 6° longitude. The UTM grid zone is designated as  $J_9$ .
- (ii) *GTN-G region*: The GTN-G region in which the glacial lake is located is termed the GTN-G region feature, and is characterised by  $J_{10}$ . These regions are essential in analyzing the variations in glaciers and other attributes in a region. Once the various features are excerpted from the database, they are combined to obtain a feature vector as given below,

$$J = \{J_1, J_2, \dots, J_{10}\}$$
 (3)

where,  $J_1$  is the latitude,  $J_2$  specifies the longitude,  $J_3$ symbolises the altitude,  $J_4$  refers to the lake area,  $J_5$ characterises the perimeter of the lake, J6 denotes the water volume,  $J_7$  represents the lake type,  $J_8$  is the uncertainty feature,  $J_9$  exemplifies the UTM grid zone, and  $J_{10}$  terms the GTN-G region. The feature vector is generated J and the shape index matrix N is applied to the proposed ShCNNFDMN for hazard assessment.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment
Type: text

A hazard assessment is carried out to find the probability that the water will be released from a glacial lake. This process is carried out by identifying the potentially hazardous lake first and then assessing the probability of water release based on the hazard index of the lake from the features extracted J. Here, hazard assessment is carried out by using the proposed ShCNNFDMN, which is created by combining the ShCNN (Ren et al. 2015) with DMN (Sun, Su, and Wang 2018) based on regression analysis. A statistical

method for determining the relationships between variables in a given set of data is regression analysis. It can evaluate the statistical significance of the association, or the probability that the correlation is the product of chance, as well as show how strong the relationship is. One of the two main purposes of regression analysis is to either predict the value of the dependent variable for those for whom some information about the explanatory factors is available, or to assess the effect of an explanatory variable on the dependent variable. The ShCNNFDMN is fed with the shape index matrix N and a feature is generated J for assessing the GLOF hazard. The ShCNNFDMN comprises three parts, such as the ShCNN model, the DMN model, and the ShCNNFDMN layer. Initially, the shape index matrix N is subjected to the ShCNN model, which classifies the hazard into either of the five categories, such as VH, H, M, L, and VL. Thereafter, the output of the ShCNN is forwarded to the ShCNNFDMN layer, which fuses the output of the ShCNN with the feature vector *J* based on regression modelling, which is applied to determining the relationship between the two inputs, thereby enhancing the effectiveness of hazard assessment. Later, the fused output generated by the ShCNNFDMN layer is subjected to the DMN model for hazard assessments along with the shape index matrix N. Here, regression modelling is accomplished by applying the concept of Fractional Calculus (FC) (Bhaladhare and Jinwala 2014). Figure 2 demonstrates the architectural view of the proposed ShCNNFDMN for hazard assessment, and the various processes undertaken are expounded on in the ensuing subsections.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.1. ShCNNmodel
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Initially, the hazard assessment is accomplished by using the ShCNN (Ren et al. 2015) by applying the shape index matrix N. The ShCNN is a type of CNN that is developed by incorporating the Shepard technique for altering the basic CNN to attain Translation Variant Interpolation (TVI). The key advantage of using the ShCNN is its low computational cost and its ability in realising end-to-end TVI operations for intermittently spaced data. The network attains superior results by adding a smaller number of feature maps and optimisation of the TVI process. The Shepard structure computes the weights of the familiar pixels based on the spatial distance between them and the target pixels differently, and its convolution form is formulated

$$H_1 k = \begin{cases} (Z*N)_k / (Z*L)_k & \text{if } L_k = 0\\ N_k & \text{if } L_k = 1 \end{cases}$$
 (4)


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.1. ShCNNmodel
Type: figure
Figure

Image /page/7/Figure/3 description: An architectural diagram illustrating the proposed ShCNNFDMN model for hazard assessment. The diagram shows a multi-stream process. In the top stream, a 'Shape index matrix N' is fed into an 'ShCNN model', which is represented by a linear sequence of seven blocks, producing 'Output H1'. In the middle stream, a 'Feature vector J' is input to the 'ShCNNFDMN layer'. This layer, which also receives 'Output H1', contains two components: 'Regression Modeling' and 'Fusion'. The output of this layer is 'Output H2'. In the bottom stream, the 'Shape index matrix N' is also input into a 'DMN model'. This model is depicted as a more complex structure with three rows of interconnected blocks. The final output of the entire system, originating from the DMN model, is the 'Risk level R'.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.1. ShCNNmodel
Type: figure
Figure: Figure 2. Architectural view of the proposed ShCNNFDMN for hazard assessment.

Figure 2. Architectural view of the proposed ShCNNFDMN for hazard assessment.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.1. ShCNNmodel
Type: text

wherein, N represents the shape index matrix to the ShCNN, k signifies the image coordinate,  $H_{1k}$  refers to the output, \* implies the convolution function, L characterises the binary operator, which has a value  $L_k=0$  in case the pixel values are not known, and Z refers to the kernel function that has a weight inversely proportional to the separation among the pixel under consideration and  $Z_k=1$ .

The characterisation of the convolutional kernel is the major factor that affects the interpolation result in the Shepard framework, and so a Shepard interpolation layer is employed to allow a kernel design that has good flexibility and is data-driven.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.1. ShCNNmodel > Shepard interpolation layer:
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The expression given below describes the Shepard interpolation layer's feed-forward pass,

$$G_{j}^{d}(G^{d-1}, L^{d}) = \alpha \left( \sum_{l} \frac{Z_{jl}^{d} * G_{l}^{d-1}}{Z_{jl}^{d} * L_{l}^{d}} + p^{d} \right), \quad d = 1, 2, 3, \dots \tag{5}$$

Here, d represents the index of layer,  $G^{d-1}$  indicates the input of the present layer, whose mask is characterised as  $L^d$ , the term j in  $G_j^{d-1}$  and  $G_j^d$  denotes the feature map index in the (d-1) th and dth layer, respectively,  $Z_{il}$  signifies the trainable parameters and convolution of

[Icon of a ship in a circle]

 $Z_{il}$  is performed with the mask of the present layer  $L^d$  in the denominator and activations of the final layer in the numerator.  $G^{d-1}$  can signify the feature maps of the convolution or pooling layers of the CNN, and this can also be considered as the preceding Shepard interpolation layer. The term  $\alpha$  is used to impose non-linearity in the network and *p* symbolises bias.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.1. ShCNNmodel > Shepard interpolation layer:
Type: figure
Figure: Figure 3 explicates the structure of the ShCNN, where it is applied with the shape index matrix N. The various inputs, such as feature maps/images, and masks representing the position where interpolation must happen are fed as input to the Shepard interpolation layer. Any complex interpolation operator can be constructed by applying the interpolation layer in a repeated manner with several non-linear layers. The mask refers to the binary map which has a value "0" for missing areas and "1" for familiar areas, and the same kernel is used with the mask as well as the image. The insignificant values of the preceding convolved mask $Z^d L^d$ are zeroed and a threshold is applied to generate the mask of the (d+1)th layer. Shepard interpolation layer with multiple stages is required to learn the sophisticated manner of propagation in processes where relatively bigger missing regions, like inpainting, occur. The output generated by the ShCNN is represented as $H_1$ .

Figure 3 explicates the structure of the ShCNN, where it is applied with the shape index matrix N. The various inputs, such as feature maps/images, and masks representing the position where interpolation must happen are fed as input to the Shepard interpolation layer. Any complex interpolation operator can be constructed by applying the interpolation layer in a repeated manner with several non-linear layers. The mask refers to the binary map which has a value "0" for missing areas and "1" for familiar areas, and the same kernel is used with the mask as well as the image. The insignificant values of the preceding convolved mask  $Z^d*L^d$  are zeroed and a threshold is applied to generate the mask of the (d+1)th layer. Shepard interpolation layer with multiple stages is required to learn the sophisticated manner of propagation in processes where relatively bigger missing regions, like inpainting, occur. The output generated by the ShCNN is represented as  $H_1$ .


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.2. ShCNNFDMN layer
Type: text

Once the output of the ShCNNH1 is obtained, it is fed to the ShCNNFDMN layer together with the location properties, lake-based properties, and global properties of the lake. One of the main advantage of ShCNNFDMN layer is simple and robust. The location properties are given by  $P_1 = \{J_1, J_2, J_3\}$  which include the latitude  $J_1$ , longitude  $J_2$ , and altitude  $J_3$ . Further, the lake-based properties are given by  $P_2 = \{J_4, J_5, J_6, J_7, J_8\}$  that comprise the lake area  $J_4$ , perimeter  $J_5$ , water volume  $J_6$ , lake type  $J_7$ , and uncertainty  $J_8$ . The global properties of the lake  $P_3 = \{J_9, J_{10}\}$  incorporate the UTM grid zone  $J_9$  and GTN-G region  $J_{10}$ . Here, the features are fused with the classified output of the ShCNN by using the concept of regression modelling. The relationship between the features and the classified output can be determined by using regression modelling, which is carried out here by utilising FC (Bhaladhare and Jinwala 2014), which is a subdivision of applied mathematics that solves the integral and derivative equations by using the Laplace transform. Here, the problem is first solved by converting it into the Laplace domain and later, the actual solution is determined by applying inverse transform. Here, the features excerpted as considered to be taken at various time intervals, and the

weighted features are combined with the classified output of the ShCNN. This process is formulated as follows,

At the interval t, the output of the ShCNNFDMN layer is expressed as,

$$g = \sum_{q=1}^{n_1} P_{1q} \omega_q \tag{6}$$

Here,  $P_1$  refers to the location properties of the lake comprising latitude, longitude, and altitude,  $n_1$  refers to the number of location properties, and  $\omega$  refers to the weight coefficients.

When the time interval t-1 is considered, then the output of the ShCNNFDMN layer is obtained by considering the lake-based properties, and this is mathematically modelled as,

$$g_1 = \sum_{q=1}^{n_2} P_{2q} \omega_q \tag{7}$$

where,  $P_2$  symbolises the lake-based properties and  $n_2$ represents the number of lake-based properties. Further, the output of the ShCNNFDMN layer at time instance t-2 is formulated based on the global properties of the glacial lake, and is modelled as,

$$g_2 = \sum_{q=1}^{n_3} P_{3q} \omega_q \tag{8}$$

Here,  $P_3$  represents the global properties of the lake, such as the UTM grid zone, and GTN-G region, and  $n_3$  characterises the number of global properties.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.2. ShCNNFDMN layer
Type: text

where , $ P_2 $ symbolises the lake - based properties and $ n_2 $ represents the number of lake - based properties . Further , the output of the ShCNNFDMN layer at time instance t - 2 is formulated based on the global properties of the glacial lake , and is modelled as , $ $ g_2 = \ sum_ { q = 1 } ^ { n_3 } P_ { 3q } \ omega_q \ tag { 8 } $ $ Here , $ P_3 $ represents the global properties of the lake , such as the UTM grid zone , and GTN - G region , and $ n_3 $ characterises the number of global properties .

Moreover, the output of the ShCNNFDMN at  $(t-3)^{th}$  intervals is considered to be the classified layer of the  $ShCNNH_1$ , and these are combined by using FC. Applying the concept of FC (Bhaladhare and Jinwala 2014),

$$\begin{aligned} y(t+1) &= u \cdot y(t) + \frac{1}{2}u \cdot y(t-1) \\ &\qquad + \frac{1}{6}(1-u)y(t-2) \\ &\qquad + \frac{1}{24}u(1-u)(2-u) \cdot y(t-3) \end{aligned}\tag{9}$$

Substituting the respective values ShCNNFDMN layer, the equation depicted above can be written as,

$$H_2 = u \cdot g + \frac{1}{2}u \cdot g_1 + \frac{1}{6}(1 - u)g_2 + \frac{1}{24}u(1 - u)(2 - u) \cdot H_1$$
 (10)

Shape index matrix N


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.2. ShCNNFDMN layer
Type: figure
Figure

Image /page/9/Figure/4 description: A diagram illustrating the architecture of a neural network. The data flows from left to right through a series of layers. The sequence of layers and the dimensions of the data after each layer are as follows: two light blue 'Conv layers' resulting in dimensions of 1x26x32 and 1x26x64 respectively; a light yellow 'Leaky ReLU layer' with an output of 1x26x64; a light blue 'Maxpooling layer' resulting in 1x13x64; an olive green 'Flatten layer' with an output of 1x832; and two light yellow 'Dense layers' with outputs of 1x32 and 1x5. The final output of the network is labeled 'Output H₁'.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.2. ShCNNFDMN layer
Type: figure
Figure: Figure 3. Structure of the ShCNN.

Figure 3. Structure of the ShCNN.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.2. ShCNNFDMN layer
Type: text

Applying the values of g,  $g_1$ , and  $g_2$  from equations (6-8),

$$\begin{aligned}H_{2} ={}& u \cdot \sum_{q=1}^{n_{1}} P_{1q} \omega_{q} + \frac{1}{2} u \cdot \sum_{q=1}^{n_{2}} P_{2q} \omega_{q} \\ & + \frac{1}{6} (1 - u) \sum_{q=1}^{n_{3}} P_{3q} \omega_{q} \\ & + \frac{1}{24} u (1 - u) (2 - u) \cdot H_{1}\end{aligned}\qquad (11)$$

Here,  $H_1$  characterises the output of the ShCNN and is found using equation (4),  $H_2$  which signifies the output generated by the ShCNNFDMN layer, and u symbolises a constant that signifies the derivative order in FC.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.3. DMN model
Type: text

The DMN is applied with the shape index matrix N, and the output  $H_2$  produced by the ShCNNFDMN layer for performing hazard assessment. The output produced by the ShCNN is also a hazard assessment level, and the output produced by the ShCNN is combined with the features and given to the DMN, thereby boosting the efficiency of hazard assessment. The ShCNN and DMN perform classification at various stages, and thus in the DMN, more refined features are used along with the shape-indexed matrix, which minimises the classification cost as well. Here, the DMN (Sun, Su, and Wang 2018) is mainly applied for hazard assessment due to its ability to produce superior outcomes in resource-constrained scenarios. DMN predict more consistently and accurately. The DMN is applied with an inputS;  $(S = \{N, H_2\})$  created by considering the shape indexed matrix N and the output of the

ShCNNFDMN layer  $H_2$ , and the various processes taking place in the DMN are explicated as given below,

$$b_{x,y}^{1} = \max_{y \in [1,v_1]} S^T V_{\dots xy} + w_{xy}$$
 (12)

$$b_{x,y}^{2}T = \max_{y \in [1,v_{2}]} b_{x,y}^{1}TV_{...xy} + w_{xy}$$
 (13)

$$b_{x,y}^{e}T = \max_{v \in [1,v_e]} b_{x,y}^{e-1}TV_{...xy} + w_{xy}$$
 (14)

$$b_{x,y}^{f}T = \max_{y \in [1,y_f]} b_{x,y}^{f-1}TV_{\dots xy} + w_{xy}$$
 (15)

$$R_{x} = \max_{y \in [1, y_{f}]} b_{x, y}^{f} \tag{16}$$

Here, x refers to the total count of layers in DMN,  $A_x$  refers to the hazard assessed by the DMN,  $v_e$  designates the overall count of units in the  $e^{th}$  layer,  $w_{xy}$  characterises the bias,  $V_{xy}$  symbolises the weight, and  $b_{x,y}^e$  designates the output produced by the eth layer. As depicted in the above equation, a max-pooling function is used by the DMN, and the maximal value of the output generated in each layer is fed to the consecutive layers. When v > 2 the DMN can estimate any conventional nonlinear activation functions. The architectural view of the DMN is depicted in Figure 4 and the output produced is indicated as R.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.4. Risk management using the proposed ShCNNFDMN
Type: text

After the hazard level *R* is identified by the ShCNNFDMN, risk assessment is carried out based on the hazard levels *R*


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.4. Risk management using the proposed ShCNNFDMN
Type: figure
Figure

Image /page/10/Figure/1 description: The number 106 is shown in black text against a white background.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.4. Risk management using the proposed ShCNNFDMN
Type: figure
Figure

Image /page/10/Figure/2 description: A diagram illustrating the architecture of a convolutional neural network. The model processes an input labeled 'S = {N, H₂}' through a series of layers to output a 'Risk level R'. The architecture is laid out in three rows. The first row consists of an Input layer (64x64x3), a Convolution layer (62x62x64), a Lamda layer (62x62x32), a Batch Normalization layer (62x62x32), and a Max pooling layer (31x31x32). The second row continues with a Convolution layer (29x29x128), a Lamda layer (29x29x64), a Batch Normalization layer (29x29x64), a Max pooling layer (14x14x64), a Dropout layer (14x14x64), and another Convolution layer (12x12x256). The third row includes a Lamda layer (12x12x64), a Batch Normalization layer (12x12x64), a Dropout layer (6x6x64), a Flatten layer (1x2304), and a Dense layer (1x1) which produces the final output. A legend at the bottom clarifies the layer types corresponding to different colors: Input layer, Lamda, Max pooling layer, Flatten, Convolution layer, Batch Normalization layer, Dropout layer, and Dense.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.4. Risk management using the proposed ShCNNFDMN
Type: figure
Figure: Figure 4. Architectural view of the DMN.

Figure 4. Architectural view of the DMN.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.4. Risk management using the proposed ShCNNFDMN
Type: text

and various feature attributes N, and J. Risk assessment is extremely essential as it gives a clear picture of the aftereffects of a GLOF, thereby helping in taking effective risk reduction strategies. Glacial lakes that are assessed to be under HVH hazard levels pose a greater risk to the communities residing downstream (Zheng et al. 2021). Here, risk assessment is performed using the ShCNN based on the risk index, which can be obtained considering the exposure index and the hazard index. The exposure index refers to the potency of GLOF causing devastation to the infrastructures as well as life and hazard index terms the combined magnitude and likelihood of the GLOF. The ShCNNFDMN is already elucidated in section 3.3., and combines the ShCNN (Ren et al. 2015) and the DMN (Sun, Su, and Wang 2018) using regression analysis.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion
Type: text

The results obtained during the experimentation of the ShCNNFDMN for GLOF risk assessment are detailed in this section. Further, the experimental set-up, evaluation measures, dataset, and analysis of the approach are also demonstrated.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.1. Experimental set-up
Type: text

The ShCNNFDMN for GLOF risk assessment proposed in this work is implemented on a system with the Python language using the glacial lake inventories dataset (https://zenodo.org/record/4477945#. ZGXf7nZBxPZ). Table 1 shows the experimental parameters of the proposed method.

4. Results and Discussion
4.1. Experimental set-up


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.1. Experimental set-up
Type: table
Table: Table 1. Experimental parameters of the proposed method.

| Methods          | Physical hydrodynamic model | GLOF Hazard assessment | Lake and breach Hazard assessment | HEC-RAS | Two-dimensional hydrodynamic model | Scenario-based multi-source GLOF | Proposed ShCNNFDMN |
|------------------|-----------------------------------|---------------------------|--------------------------------------|---------|---------------------------------------|-------------------------------------|-----------------------|
| Epochs           | 20                                | 20                        | 25                                   | 20      | 20                                    | 20                                  | 30                    |
| Batch size       | 32                                | 32                        | 32                                   | 32      | 32                                    | 32                                  | 64                    |
| Learning rate | 0.001                             | 0.001                     | 0.001                                | 0.001   | 0.001                                 | 0.001                               | 0.001                 |

Methods
Physical hydrodynamic model
GLOF Hazard assessment
Lake and breach Hazard assessment
HEC-RAS
Two-dimensional hydrodynamic model
Scenario-based multi-source GLOF
Proposed ShCNNFDMN


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.1. Experimental set-up
Type: text

The bold values represent the best performance.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.1. Experimental set-up
Type: figure
Figure

Image /page/11/Figure/3 description: A geographical map showing the distribution of glacial lakes over a Digital Elevation Model (DEM) of a mountainous region. The map is bounded by coordinates from 70°0'0"E to 100°0'0"E longitude and from 20°0'0"N to 40°0'0"N latitude. A compass rose is present in the upper right corner. The legend indicates that the blue areas represent 'Glacial Lakes'. The DEM values, shown in grayscale, range from a 'Low' of 232 to a 'High' of 8233. The map includes a scale bar marked up to 1,240 Kilometers and a textual scale of '1 cm = 210 km'.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.1. Experimental set-up
Type: figure
Figure: Figure 5. Glacial Lake Dataset.

Figure 5. Glacial Lake Dataset.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.2. Dataset description
Type: text

The data used for the experimentation of the ShCNNFDMN is acquired from the glacial lake inventories dataset and has been mapped in Figure 5 (https://zenodo.org/record/4477945#.ZGXf7nZBxPZ). This dataset comprises the data of the glacial lakes present in the Third Pole region. The data encompassed are acquired over a period of 15 years from 1990 to 2015, Further, the data is modelled for considering future conditions for 2050 and 2100 on the Third Pole under an ice-free scenario, Representative Concentration Pathway (RCP) 8.5, RCP 4.5, and RCP 2.6. It contains various data regarding the glacial lake shape, location, uncertainty, lake water volume, hazard value, risk value, exposure value, etc.

4. Results and Discussion
4.2. Dataset description


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.3. Evaluation measures
Type: text

Two parameters, such as Hazard modelling error, and Risk prediction error are considered to evaluate the supremacy of the ShCNNFDMN.

(i) *Hazard modelling error*: The hazard modelling error is measured by finding the deviation of the predicted hazard value by the ShCNNFDMN from the expected values.

$$HME = \frac{\sum_{i=1}^{n} |y_i - x_i|}{n}.$$
 (17)

where, HME refers to the Hazard Modelling Error,

 $y_i$  refers to the prediction, $x_i$  refers to the true value, and n designates the total number of data.

(ii) *Risk prediction error:* The risk prediction error is also found similar to the hazard modelling error by determining the difference between the predicted risk value from the anticipated risk value.

$$RPE = \frac{\sum_{i=1}^{n} |y_i - x_i|}{n}.$$
 (18)

where, *RPE* refers to the Risk Prediction Error.

(iii) *Mean Average Error (MAE):* The MAE, is a statistical measure of inaccuracies between paired observations that represent the same occurrence.

$$MAE = \frac{\sum_{i=1}^{n} |y_i - x_i|}{n}.$$
 (19)

where, MAE refers to the Mean Average Error.

(iv) *R-Squared:* The percentage of a dependent variable's variance that can be accounted for by an independent variable is expressed statistically as R-squared.

$$R^2 = 1 - \frac{RSS}{TSS}. (20)$$

where, RSS refers to the sum of squares of residuals, and TSS refers to the total sum of squares.

4. Results and Discussion
4.3. Evaluation measures


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.4. Performance analysis
Type: text

The performance of the ShCNNFDMN is examined by considering the values of Hazard modelling error, and Risk

prediction error determined when different k-fold values and training data percentages are used for various iterations.

4. Results and Discussion
4.4. Performance analysis


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.4. Performance analysis > 4.4.1. Based on k-fold
Type: figure
Figure: Figure 6 demonstrates the performance assessment of the ShCNNFDMN while varying the k-fold values. In Figure 6 (a), the analysis of the ShCNNFDMN based on hazard modelling error is exhibited. With a k-fold of 9, the value of hazard prediction error measured by the ShCNNFDMN is 0.558, 0.526, 0.518, 0.505, and 0.487, corresponding to 20, 40, 60, 80, and 100 iterations. Likewise, the assessment of the ShCNNFDMN in terms of risk prediction error is illustrated in Figure 6(b). The ShCNNFDMN is observed to have attained a risk prediction error of 0.495 for 20 iterations, 0.507 for 40 iterations, 0.497 for 60 iterations, 0.484 for 80 iterations, and 0.428 for 100 iterations with k-fold of 9. In Figure 6(c), the analysis of the ShCNNFDMN based on MAE is showed. With a k-fold of 9, the value of MAE measured by the ShCNNFDMN is 0.450, 0.414, 0.410, 0.395, and 0.370,

Figure 6 demonstrates the performance assessment of the ShCNNFDMN while varying the k-fold values. In Figure 6 (a), the analysis of the ShCNNFDMN based on hazard modelling error is exhibited. With a k-fold of 9, the value of hazard prediction error measured by the ShCNNFDMN is 0.558, 0.526, 0.518, 0.505, and 0.487, corresponding to 20, 40, 60, 80, and 100 iterations. Likewise, the assessment of the ShCNNFDMN in terms of risk prediction error is illustrated in Figure 6(b). The ShCNNFDMN is observed to have attained a risk prediction error of 0.495 for 20 iterations, 0.507 for 40 iterations, 0.497 for 60 iterations, 0.484 for 80 iterations, and 0.428 for 100 iterations with k-fold of 9. In Figure 6(c), the analysis of the ShCNNFDMN based on MAE is showed. With a k-fold of 9, the value of MAE measured by the ShCNNFDMN is 0.450, 0.414, 0.410, 0.395, and 0.370,

4. Results and Discussion
4.4. Performance analysis
4.4.1. Based on k-fold


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.4. Performance analysis > 4.4.1. Based on k-fold
Type: text

corresponding to 20, 40, 60, 80, and 100 iterations. Figure 6 (d) explicates the assessment of the ShCNNFDMN on the basis of R-Squared. For k-fold of 9, the ShCNNFDMN recorded R-Squared of 0.346 for 20 iterations, 0.331 for 40 iterations, 0.324 for 60 iterations, 0.312 for 80 iterations, and 0.292 for 100 iterations.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.4. Performance analysis > 4.4.2. Considering training data
Type: text

The evaluation of the ShCNNFDMN considers the hazard modelling error and risk prediction error while taking into account various training data percentages. Figure 7(a) explicates the assessment of the ShCNNFDMN on the basis of hazard modelling error. For 90% of training data, the ShCNNFDMN recorded hazard modelling error of 0.644 for 20 iterations, 0.582 for 40 iterations, 0.561 for 60 iterations, 0.540 for 80 iterations, and 0.484 for 100 iterations. Further, the assessment of the ShCNNFDMN concerning the risk prediction error is exhibited in Figure 7(b). The risk prediction error measured by the ShCNNFDMN

4. Results and Discussion
4.4. Performance analysis
4.4.2. Considering training data


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.4. Performance analysis > 4.4.2. Considering training data
Type: figure
Figure

Image /page/12/Figure/9 description: The image displays four line graphs, labeled (a), (b), (c), and (d), showing the performance assessment of a model named ShCNNFDM. Each graph plots a different performance metric against 'K-Fold' on the x-axis, which ranges from 5 to 9. Each graph includes five lines representing the model run with a different number of iterations: 20 (blue with 'x' markers), 40 (pink with '\*' markers), 60 (green with right-pointing triangle markers), 80 (teal with left-pointing triangle markers), and 100 (red with circle markers).


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.4. Performance analysis > 4.4.2. Considering training data
Type: text

(a) The top-left graph shows 'Hazard modelling error' on the y-axis, from 0.50 to 0.70. For all lines, the error generally decreases as K-Fold increases. The error is lowest for the model with 100 iterations (red line, from ~0.53 to ~0.48) and highest for the model with 20 iterations (blue line, from ~0.71 to ~0.56).

(b) The top-right graph shows 'Risk prediction error' on the y-axis, from 0.45 to 0.70. Similar to the first graph, the error decreases as K-Fold increases and as the number of iterations increases. The 100-iteration model has the lowest error (red line, from ~0.54 to ~0.43), while the 20-iteration model has the highest (blue line, from ~0.72 to ~0.51).

(c) The bottom-left graph shows 'MAE' (Mean Absolute Error) on the y-axis, from 0.38 to 0.52. The MAE decreases as K-Fold increases and as the number of iterations increases. The 100-iteration model performs best with the lowest MAE (red line, from ~0.45 to ~0.37), and the 20-iteration model performs worst with the highest MAE (blue line, from ~0.52 to ~0.45).

(d) The bottom-right graph shows 'R-squared' on the y-axis, from 0.30 to 0.44. In this graph, the R-squared value decreases as K-Fold increases for all models. Contrary to the error metrics, a higher R-squared value is better, and the model with fewer iterations performs better. The 20-iteration model has the highest R-squared (blue line, from ~0.44 to ~0.34), while the 100-iteration model has the lowest (red line, from ~0.36 to ~0.29).


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.4. Performance analysis > 4.4.2. Considering training data
Type: figure
Figure: Figure 6. Performance assessment of the ShCNNFDMN based on (a) Hazard modelling error, (b) Risk prediction error, (c) MAE, and (d) R-Squared.

Figure 6. Performance assessment of the ShCNNFDMN based on (a) Hazard modelling error, (b) Risk prediction error, (c) MAE, and (d) R-Squared.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.4. Performance analysis > 4.4.2. Considering training data
Type: figure
Figure

Image /page/13/Figure/3 description: The image contains four line graphs, labeled (a), (b), (c), and (d), which show the performance assessment of a model called ShCNNFDMN. Each graph plots a different performance metric against the percentage of training data, which ranges from 50% to 90% on the x-axis. All graphs compare five versions of the model based on the number of iterations: 20, 40, 60, 80, and 100.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.4. Performance analysis > 4.4.2. Considering training data
Type: text

(a) The first graph plots 'Hazard modelling error' on the y-axis from 0.50 to 0.75. For all lines, the error decreases as the training data increases. The error is lowest for 100 iterations (red line, from ~0.56 to ~0.48) and highest for 20 iterations (blue line, from ~0.75 to ~0.64).

(b) The second graph plots 'Risk prediction error' on the y-axis from 0.45 to 0.75. The trend is similar to the first graph, with the error decreasing with more training data and more iterations. The 100-iteration line drops from ~0.57 to ~0.44, while the 20-iteration line drops from ~0.74 to ~0.62.

(c) The third graph plots 'MAE' (Mean Absolute Error) on the y-axis from 0.375 to 0.525. Again, the error decreases as training data and the number of iterations increase. The 100-iteration line goes from ~0.43 to ~0.36, and the 20-iteration line goes from ~0.53 to ~0.42.

(d) The fourth graph plots 'R-squared' on the y-axis from 0.30 to 0.42. In this case, the R-squared value decreases as the training data percentage increases. The model with 20 iterations has the highest R-squared values (from ~0.425 to ~0.335), while the model with 100 iterations has the lowest (from ~0.39 to ~0.295).

The caption below reads: 'Figure 7. Performance assessment of the ShCNNFDMN based on (a) Hazard modelling error, (b) Risk prediction error, (c) MAE, and (d) R-squared.'


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.4. Performance analysis > 4.4.2. Considering training data
Type: figure
Figure: Figure 7. Performance assessment of the ShCNNFDMN based on (a) Hazard modelling error, (b) Risk prediction error, (c) MAE, and (d) R-Squared based on training data.

Figure 7. Performance assessment of the ShCNNFDMN based on (a) Hazard modelling error, (b) Risk prediction error, (c) MAE, and (d) R-Squared based on training data.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.4. Performance analysis > 4.4.2. Considering training data
Type: text

with 20, 40, 60, 80, and 100 iterations is 0.620, 0.569, 0.524, 0.463, and 0.436, respectively, for 90% of training data. In Figure 7(c), the analysis of the ShCNNFDMN based on MAE is exhibited. With 90% of training data, the value of MAE measured by the ShCNNFDMN is 0.423, 0.422, 0.417, 0.387, and 0.364, corresponding to 20, 40, 60, 80, and 100 iterations. Likewise, the assessment of the ShCNNFDMN in terms of R-Squared is illustrated in Figure 7(d). The ShCNNFDMN is observed to have attained a R-Squared of 0.338 for 20 iterations, 0.318 for 40 iterations, 0.318 for 60 iterations, 0.311 for 80 iterations, and 0.296 for 100 iterations with 90% of training data.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.5. Comparative techniques
Type: text

The efficiency of the ShCNNFDMN is investigated by comparing the proposed technique with other prevailing

GLOF risk assessment methods, such as the Physical hydrodynamic model (Sattar et al. 2021), GLOF Hazard assessment (Liu et al. 2020), Lake and breach Hazard assessment (Emmer and Vilímek 2013), HEC-RAS (Wang et al. 2018), Two-dimensional hydrodynamic model, and Scenario-based multi-source GLOF.

4. Results and Discussion
4.5. Comparative techniques


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.6. Comparative analyis
Type: text

The analysis of the ShCNNFDMN is carried out to examine its superiority in performing GLOF risk/hazard assessment by taking into consideration different values of training data and k-fold.

4. Results and Discussion
4.6. Comparative analyis


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.6. Comparative analyis > 4.6.1. Using K-fold
Type: text

The examination of the ShCNNFDMN based on k-fold is with respect to hazard modelling error and risk prediction error is displayed in Figure 8. The hazard

4. Results and Discussion
4.6. Comparative analyis
4.6.1. Using K-fold


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.6. Comparative analyis > 4.6.1. Using K-fold
Type: figure
Figure

Image /page/14/Figure/1 description: Two line graphs, labeled (a) and (b), compare the performance of seven different models. Both graphs plot an error metric on the y-axis against 'K-Fold' on the x-axis, which ranges from 5 to 9. Graph (a) shows 'Hazard modelling error' on the y-axis, ranging from 0.50 to 0.75. Graph (b) shows 'Risk prediction error' on the y-axis, ranging from 0.45 to 0.75. The seven models compared are: 'Physical hydrodynamic model' (blue), 'GLOF Hazard assessment' (pink), 'Lake and breach Hazard assessment' (green), 'HEC-RAS' (cyan), 'Two-dimensional hydrodynamic model' (red), 'Scenario-based multi-source GLOF' (yellow), and 'Proposed ShCNNFDMN' (dark gray). In both graphs, the 'Proposed ShCNNFDMN' model consistently has the lowest error across all K-Fold values. In graph (a), its error decreases from approximately 0.52 at K-Fold 5 to 0.47 at K-Fold 9. In graph (b), its error decreases from approximately 0.56 at K-Fold 5 to 0.43 at K-Fold 9. The other models generally show higher and more variable error rates.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.6. Comparative analyis > 4.6.1. Using K-fold
Type: figure
Figure

Image /page/14/Figure/2 description: A line graph, labeled (c), plots the Mean Absolute Error (MAE) on the y-axis against the K-Fold value on the x-axis. The x-axis ranges from 5 to 9, while the y-axis ranges from 0.375 to 0.525. The graph compares the performance of seven different models, each represented by a colored line with unique markers. The legend identifies six of the models: 'GLOF Hazard assessment' (dark blue line with 'x' markers), 'Lake and breach Hazard assessment' (pink line with star markers), 'HEC-RAS' (cyan line with triangle markers), 'Two-dimensional hydrodynamic model' (red line with circle markers), 'Scenario-based multi-source GLOF' (yellow line with diamond markers), and 'Proposed ShCNNFDMN' (gray line with pentagon markers). A seventh line, green with circular markers, is also present but not identified in the legend. The 'Proposed ShCNNFDMN' model consistently shows the lowest MAE across all K-Fold values, decreasing from approximately 0.445 at K-Fold 5 to 0.362 at K-Fold 9. Most other models also show a general downward trend in MAE as the K-Fold value increases.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.6. Comparative analyis > 4.6.1. Using K-fold
Type: figure
Figure

Image /page/14/Figure/3 description: A line graph, labeled (d), showing the relationship between R-squared values and K-Fold for seven different models. The x-axis is labeled 'K-Fold' and ranges from 5 to 9. The y-axis is labeled 'R-squared' and ranges from 0.30 to 0.44. The graph displays seven lines, each representing a different model, and all lines show a downward trend, indicating that the R-squared value decreases as the K-Fold value increases. The models and their approximate data points are as follows: 'Physical hydrodynamic model' (blue line with x markers) starts at approximately R-squared=0.445 at K-Fold=5 and ends at 0.348 at K-Fold=9. 'GLOF Hazard assessment' (pink line with star markers) starts at 0.443 and ends at 0.345. 'Lake and breach Hazard assessment' (green line with circle markers) starts at 0.435 and ends at 0.33. 'HEC-RAS' (cyan line with triangle markers) starts at 0.42 and ends at 0.33. 'Two-dimensional hydrodynamic model' (red line with circle markers) starts at 0.422 and ends at 0.325. 'Scenario-based multi-source GLOF' (yellow line with diamond markers) starts at 0.412 and ends at 0.312. 'Proposed ShCNNFDMN' (gray line with pentagon markers) consistently has the lowest R-squared values, starting at 0.398 and ending at 0.30.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.6. Comparative analyis > 4.6.1. Using K-fold
Type: figure
Figure: Figure 8. Comparative assessment of the ShCNNFDMN based on (a) Hazard modelling error, (b) Risk prediction error, (c) MAE, and (d) R-Squared considering k-fold.

Figure 8. Comparative assessment of the ShCNNFDMN based on (a) Hazard modelling error, (b) Risk prediction error, (c) MAE, and (d) R-Squared considering k-fold.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.6. Comparative analyis > 4.6.1. Using K-fold
Type: text

modelling error-based assessment of the ShCNNFDMN is demonstrated in Figure 8(a). The ShCNNFDMN is found to have produced a hazard modelling error of 0.483with k-fold of 8, while the prevailing risk assessment approaches, such as the Physical hydrodynamic model, GLOF Hazard assessment, Lake and breach assessment, HEC-RAS, Two-dimensional hydrodynamic model, and Scenario-based multi-source GLOF, measured hazard modelling error of 0.620, 0.594, 0.619, 0.612, 0.583, and 0.516, correspondingly. In Figure 8(b), the investigation of the ShCNNFDMN with respect to risk prediction error is presented, For a k-fold value of 8, the risk prediction error recorded by the Physical hydrodynamic model is 0.639, GLOF Hazard assessment is 0.554, Lake and breach Hazard assessment is 0.526, HEC-RAS is 0.544, Two-dimensional hydrodynamic model is 0.527, Scenario-based multi-source GLOF is 0.496, and ShCNNFDMN is 0.454. The MAE-based assessment ShCNNFDMN is demonstrated in Figure 8(c). The ShCNNFDMN is found to have produced a MAE of 0.379 with k-fold of 8, while the prevailing risk assessment approaches, such as the Physical hydrodynamic model, GLOF Hazard assessment, Lake and breach Hazard assessment, HEC-RAS, Two-dimensional hydrodynamic model, and Scenario-based multi-source GLOF, measured hazard modelling error of 0.470, 0.450, 0.448, 0.425, 0.416, and 0.415, correspondingly. In Figure 8(d), the investigation of the ShCNNFDMN with respect to R-Squared is presented, For a k-fold value of 8, the R-Squared recorded by the Physical hydrodynamic model is 0.366, GLOF Hazard assessment is 0.356, Lake and breach Hazard assessment is 0.355, HEC-RAS is 0.350, Two-dimensional hydrodynamic model is 0.344, Scenario-based multi-source GLOF is 0.330, and ShCNNFDMN is 0.313.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.6. Comparative analyis > 4.6.2. Based on training data
Type: figure
Figure: Figure 9 represents the analysis of the ShCNNFDMN for GLOF risk/hazard assessment considering different percentages of training data. The ShCNNFDMN is examined for its efficacy on the basis of hazard

Figure 9 represents the analysis of the ShCNNFDMN for GLOF risk/hazard assessment considering different percentages of training data. The ShCNNFDMN is examined for its efficacy on the basis of hazard

4. Results and Discussion
4.6. Comparative analyis
4.6.2. Based on training data


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.6. Comparative analyis > 4.6.2. Based on training data
Type: figure
Figure

Image /page/15/Figure/3 description: A set of four line graphs, labeled (a), (b), (c), and (d), comparing the performance of seven different models against varying amounts of training data. The x-axis for all graphs is 'Training data (%)', ranging from 50 to 90. The seven models compared are: 'Physical hydrodynamic model', 'GLOF Hazard assessment', 'Lake and breach Hazard assessment', 'HEC-RAS', 'Two-dimensional hydrodynamic model', 'Scenario-based multi-source GLOF', and 'Proposed ShCNNFDMN'.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.6. Comparative analyis > 4.6.2. Based on training data
Type: text

Graph (a) plots 'Hazard modelling error' on the y-axis (from 0.45 to 0.75). Generally, the error for all models decreases as training data increases. The 'Proposed ShCNNFDMN' model consistently has the lowest error, decreasing from approximately 0.54 to 0.48. The 'Physical hydrodynamic model' starts with the highest error at about 0.74.

Graph (b) plots 'Risk prediction error' on the y-axis (from 0.45 to 0.75). The error trends downwards for most models with more training data. The 'Proposed ShCNNFDMN' model again shows the best performance with the lowest error, dropping from about 0.57 to 0.42.

Graph (c) plots 'MAE' (Mean Absolute Error) on the y-axis (from 0.350 to 0.550). All models show a decrease in MAE as training data increases. The 'Proposed ShCNNFDMN' model has the lowest MAE, decreasing from around 0.435 to 0.36. The 'Physical hydrodynamic model' has the highest MAE across the range.

Graph (d) plots 'R-squared' on the y-axis (from 0.30 to 0.44). In this graph, the R-squared values for all models generally decrease as the percentage of training data increases. The 'Physical hydrodynamic model' has the highest R-squared values, while the 'Proposed ShCNNFDMN' has the lowest.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.6. Comparative analyis > 4.6.2. Based on training data
Type: figure
Figure: Figure 9. Comparative assessment of the ShCNNFDMN based on (a) Hazard modelling error, (b) Risk prediction error, (c) MAE, and (d) R-Squared based on training data.

Figure 9. Comparative assessment of the ShCNNFDMN based on (a) Hazard modelling error, (b) Risk prediction error, (c) MAE, and (d) R-Squared based on training data.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.6. Comparative analyis > 4.6.2. Based on training data
Type: text

modelling error and this is portrayed in Figure 9(a). For training data of 80%, the hazard modelling error produced by the Physical hydrodynamic model, GLOF Hazard assessment, Lake and breach Hazard assessment, HEC-RAS, Two-dimensional hydrodynamic model, Scenario-based multi-source GLOF, and ShCNNFDMN is 0.601, 0.613, 0.627, 0.669, 0.592, 0.542, and 0.484, respectively. Likewise, Figure 9(b) illustrates the examination of the ShCNNFDMN on the basis of risk prediction error. The risk prediction error measured by ShCNNFDMNis 0.463, for 80% of training data, whereas the other techniques produced risk prediction error of 0.563 for the Physical hydrodynamic model, 0.553 for GLOF Hazard assessment, 0.517 for Lake and breach Hazard assessment, 0.541 for HEC-RAS, 0.516 for Two-dimensional hydrodynamic model, 0.495 for Scenario-based multi-source GLOF. The ShCNNFDMN is examined for its efficacy on the basis of MAE and this is portrayed in Figure 9(c). For training data of 80%, the MAE produced by the Physical hydrodynamic model, GLOF Hazard assessment, Lake and breach Hazard assessment, HEC-RAS, Two-dimensional hydrodynamic model, Scenario-based multisource GLOF, and ShCNNFDMN is 0.473, 0.452, 0.446, 0.443, 0.440, 0.423, and 0.380, respectively. The R-Squared-based assessment of the ShCNNFDMN is demonstrated in Figure 9(d). The ShCNNFDMN is found to have produced a R-Squared of 0.307 with training data 80%, while the prevailing risk assessment approaches, such as the Physical hydrodynamic model, GLOF Hazard assessment, Lake and breach Hazard assessment, HEC-RAS, Two-dimensional hydrodynamic model, and Scenario-based multi-source GLOF, measured hazard modelling error of 0.370, 0.350, 0.349, 0.339, 0.320, and 0.318, correspondingly.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.7. Comparative discussion
Type: text

The GLOF risk/hazard assessment technique proposed in this work using ShCNNFDMN is examined for its

4. Results and Discussion
4.7. Comparative discussion


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.7. Comparative discussion
Type: table
Table: Table 2. Comparative discussion of the ShCNNFDMN.

| Variations       | Metrics                      | Physical hydrodynamic model | GLOF Hazard assessment | Lake and breach Hazard assessment | HEC- RAS | Two-dimensional hydrodynamic model | Scenario-based multi-source GLOF | Proposed ShCNNFDMM |
|------------------|------------------------------|-----------------------------------|---------------------------|-----------------------------------------|-------------|------------------------------------------|----------------------------------------|-----------------------|
| K-fold           | Hazard modelling error | 0.613                             | 0.634                     | 0.603                                   | 0.563       | 0.557                                    | 0.507                                  | 0.473                 |
|                  | Risk prediction error  | 0.554                             | 0.604                     | 0.551                                   | 0.517       | 0.483                                    | 0.467                                  | 0.427                 |
|                  | MAE                          | 0.450                             | 0.442                     | 0.437                                   | 0.436       | 0.415                                    | 0.398                                  | 0.363                 |
|                  | R-squared                    | 0.348                             | 0.343                     | 0.330                                   | 0.328       | 0.325                                    | 0.313                                  | 0.297                 |
| Training data | Hazard modelling error | 0.565                             | 0.601                     | 0.578                                   | 0.638       | 0.573                                    | 0.527                                  | 0.462          |
|                  | Risk prediction error  | 0.592                             | 0.545                     | 0.592                                   | 0.466       | 0.453                                    | 0.435                                  | 0.423          |
|                  | MAE                          | 0.439                             | 0.427                     | 0.416                                   | 0.390       | 0.382                                    | 0.371                                  | 0.358          |
|                  | R-squared                    | 0.353                             | 0.340                     | 0.328                                   | 0.313       | 0.305                                    | 0.303                                  | 0.288          |

Variations
Metrics
Physical hydrodynamic model
GLOF Hazard assessment
Lake and breach Hazard assessment
HEC- RAS
Two-dimensional hydrodynamic model
Scenario-based multi-source GLOF
Proposed ShCNNFDMM


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.7. Comparative discussion
Type: text

efficiency considering hazard modelling error and risk prediction error with respect to other related works and this is explicated in Table 2. The values of hazard modelling error, risk prediction error, MAE, and Rsquared portrayed are recorded when k-fold value of 9 and training data of 90% are considered. The superior values of 0.462, 0.423, 0.358, and 0.288 are obtained by the ShCNNFDMN assessment based on hazard modelling error, risk prediction error, MAE, and R-Squared for 90% training data. The usage of multistage classifier developed with ShCNN and DMN based on regression analysis effectively modelled the hazard and risk values, thus minimising the risk prediction error.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.7. Comparative discussion > Below are some of the factors that contribute to the proposed approach's superior performance:
Type: text

The proposed ShCNNFDMN is formulated by combining the ShCNN and DMN. The key advantage of using the ShCNN is its low computational cost and its ability in realising end-to-end TVI operations for intermittently spaced data. The network attains superior results by adding a smaller number of feature maps and optimisation of the TVI process. DMN perform classification at various stages, and thus in the DMN, more refined features are used along with the shapeindexed matrix, which minimises the classification cost. Here, the DMN is mainly applied for hazard assessment due to its ability to produce superior outcomes in resource-constrained scenarios. DMN predict


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.7. Comparative discussion > Below are some of the factors that contribute to the proposed approach's superior performance:
Type: table
Table: Table 3. ANOVA test for the proposed method.

|             | Sum of squares | Degrees of freedom | Mean squares | F    | P value   |
|-------------|----------------|-----------------------|--------------|------|-----------|
| Factor 1 | 14.35          | 3                     | 7.845        | 18.2 | 7.823e-05 |
| Error       | 6.15           | 15                    | 0.571254     | -    | -         |
| Total       | 19             | 17                    | -            | -    | -         |

Sum of squares
Degrees of freedom
Mean squares
F
P value


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.7. Comparative discussion > Below are some of the factors that contribute to the proposed approach's superior performance:
Type: text

more consistently and accurately. Thus, the proposed method ShCNNFDMN produce better performance.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 4. Results and Discussion > 4.8. Analysis of ANOVA Test
Type: text

The ANOVA test analysis is shown in Table 3. It's known as the statistical approach. It is the procedure for breaking down the data variables into distinct parts in order to conduct further tests. To get information about the independent and dependent variables, the data are grouped using ANOVA. The F test is the another name for the ANOVA test. The ANOVA formula is written as follows:

$$F = \frac{MST}{MSE}$$

Where, F = ANOVA coefficient, MST = Mean sum of squares due to treatment, MSE = Mean sum of squares due to error.

4. Results and Discussion
4.8. Analysis of ANOVA Test


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 5. Conclusion
Type: text

This paper uses a unique deep-learning network called ShCNNFDMN to propose a fresh framework for assessing GLOF risks and hazards. In this case, evaluation is done based on a number of glacial lake attributes, including geometric, geographical, lake-based, and global attributes. First, the features are taken from the input data and evaluated by the ShCNNFDMN. Regression analysis is performed using the FC idea, and the resulting ShCNNFDMN is the result of the fusion of ShCNN and DMN. Initially, the ShCNN is trained with the geometric feature of the lake that was retrieved in the form of a shape-indexed matrix for classification. The final output and features are combined using the ShCNNFDMN layer, and for fine risk assessment, the DMN and shape-indexed matrix are applied. Superior values of 0.462, 0.423, 0.358, and 0.288 are noted in the ShCNNFDMN assessment based on hazard modelling error, risk prediction error, MAE, and R-Squared. The proposed method helps to prevent major hazards and risks. The proposed method is useful in various applications, like proper infrastructure planning, and taking preventive and mitigative actions in downstream areas of glacier lakes. The limitation of the proposed method is that it does not consider more evalution metrics to evaluate the model's performance. In future work, parameters, like the geomorphic parameters of the river channel, such as slope, and moraine can be considered to improve the risk assessment efficiency. Also, some more evaluation metrics will be considered to evaluate the model's performance.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: Disclosure statement
Type: text

No potential conflict of interest was reported by the author(s).


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: Funding
Type: text

This work was supported by The International Talent Program of the Chinese Academy of Sciences [grant number 2021PC0045].


Document: Definition and classification system of glacial lake for inventory and hazards study
Type: text

Abstract: Glacial lakes are not only the important refresh water resources in alpine region, but also act as a trigger of many glacial hazards such as glacial lake outburst flood (GLOF) and debris flow. Therefore, glacial lakes play an important role on the cryosphere, climate change and alpine hazards. In this paper, the issues of glacial lake were systematically discussed, then from the view of glacial lake inventory and glacial lake hazards study, the glacial lake was defined as natural water mainly supplied by modern glacial meltwater or formed in glacier moraine's depression. Furthermore, a complete classification system of glacial lake was proposed based on its formation mechanism, topographic feature and geographical position. Glacial lakes were classified as 6 classes and 8 subclasses, i.e., glacial erosion lake (including cirque lake, glacial valley lake and other glacial erosion lake), moraine-dammed lake (including end moraine-dammed lake, lateral moraine-dammed lake and moraine thaw lake), ice-blocked lake (including advancing glacier-blocked lake and other glacier-blocked lake), supraglacial lake, subglacial lake and other glacial lake. Meanwhile, some corresponding features exhibiting on remote sensing image and quantitative indices for identifying different glacial lake types were proposed in order to build a universal and operational classification system of glacial lake.

Keywords: glacial lake; glacier; definition; classification; inventory; hazard

data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: Introduction
Type: text

Lakes refer to water bodies which are formed in land surface basin or waterlogged depression, having certain water area and water exchange being relatively slow (Ma et al., 2011). Lakes are not only the important component of the Earth's hydrosphere, but also can faithfully record the regional climate change in different time scales and human activities around

**Received:** 2017-04-23 **Accepted:** 2017-06-05

Foundation: National Natural Science Foundation of China, No.41261016, No.41561016; Opening Foundation Projection of State Key Laboratory of Cryosphere Sciences, CAS, No.SKLCS-OP-2016-10; Youth Scholar Scientific Capability Promoting Project of Northwest Normal University, No.NWNU-LKQN-14-4; Geological Survey Project of China Geological Survey, No.DD2016034206

Author: Yao Xiaojun (1980-), PhD and Associate Professor, specialized in the research of GIS and cryospheric change. E-mail: xj\_yao@nwnu.edu.cn

lake. So lake is often considered as an important information carrier to reveal global climate change and the regional response (Cenderelli and Wohl, 2001; Ding *et al.*, 2006; Yao *et al.*, 2014; Liu *et al.*, 2014). Glacial lakes, as one of the lake types, are precious fresh water resources and the natural landscape in alpine region, and yet act as a trigger of many glacial hazards (Richardson and Reynolds, 2000; Wang *et al.*, 2010). Under the background of global warming, glacier-related hazards such as glacial lake outburst flood (GLOF) and debris flow presented an increase tendency in amount and harmful intensity (Cui *et al.*, 2014). In the Tibetan Plateau, for example, there had been at least 28 GLOF events since the 1930s (Sun *et al.*, 2014; Yao *et al.*, 2014), and had characteristics of frequency increase and temporal extension after 2000. Therefore, glacial lakes have attracted widespread attention from the academia and local government (Cui *et al.*, 2014).


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: Introduction
Type: text

. Under the background of global warming , glacier - related hazards such as glacial lake outburst flood ( GLOF ) and debris flow presented an increase tendency in amount and harmful intensity ( Cui * et al . * , 2014 ) . In the Tibetan Plateau , for example , there had been at least 28 GLOF events since the 1930s ( Sun * et al . * , 2014 ; Yao * et al . * , 2014 ) , and had characteristics of frequency increase and temporal extension after 2000 . Therefore , glacial lakes have attracted widespread attention from the academia and local government ( Cui * et al . * , 2014 ) .

Globally, glacial lakes are mainly located in North America (e.g. Rocky Mountains and Coastal Mountains of Alaska, USA) (O'Connor and Costa, 1993; Clague and Evans, 2000), South America (e.g. Andes Mountains) (Carey, 2005), Europe (e.g. Iceland, the Alps and Caucasus) (Huggel et al., 2002; Björnsson, 2003; Stokes et al., 2007; Emmer et al., 2015), and Asia (e.g. Altay Mountains, Tianshan Mountains, Karakoram Range, the Himalayas) (Fujita et al., 2008; Chen et al., 2010; Janský et al., 2010; Li et al., 2011; Engel et al., 2012; Wang et al., 2012; Wang et al., 2013; Wang et al., 2016; Song et al., 2016; Song et al., 2017). The interest area of glacial lakes in China was mainly concentrated in the Himalayas (Gao et al., 2015; Liao et al., 2015), Tianshan Mountains (Wang et al., 2013), Karakoram Range (Wortmann et al., 2014), Altay Mountains (Chen et al., 2015) and southeastern Tibet (Wang et al., 2012; Song et al., 2016). And these studies focused on the change of glacial lakes (Liu et al., 2011; Wang et al., 2013; Gao et al., 2015; Song et al., 2016; Wang et al., 2016), the identification of potential dangerous glacial lakes (Cao et al., 2016; Liu et al., 2016), and the simulation of GLOFs (Le et al., 2014). In recent years, the inventory of glacial lakes in some mountainous regions had been carried out based on multi-source remote sensing images by scholars and institutions. For instance, Wang et al. (2010) and Pradeep et al. (2001) digitalized the glacial lakes in the Hindu Kush-Himalaya Range, respectively. These datasets had been an important basis for recognizing the spatial-temporal characteristics of glacial lakes change and understanding the response of glacial lakes to the climate change in this region. However, there is still great controversy about the definition and classification system of glacial lakes, which will directly lead to some difficulties for comparing the result from different researches. Some conclusions of glacial lakes change were even opposite. For example, Gao et al. (2015) and Wang et al. (2014) analyzed the glacial lakes change in the Koshi River basin from 2000 to 2010, the former believed that both number and area of glacial lakes were increasing, but the latter thought that the number was decreasing and yet the area was increasing. Even more puzzling was the fact that, the remote sensing images in 2000 used in the two studies above were the same, with the number of glacial lakes being 1228 and 1680, respectively. Therefore, it is necessary and urgent to accurately define glacial lakes, to build a complete classification system of glacial lakes and to provide corresponding features for remote sensing identification.


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 2 Glacial lake
Type: text

In the Glossary of Cryospheric Science (Qin et al., 2016), glacial lake is defined as lake

formed by glaciation. In the Wikipedia Encyclopedia (https://en.wikipedia.org), a glacial lake is a lake with origins in a melted glacier. They are formed when a glacier erodes the land, and then melts, filling the hole or space that it has created. Lü et al. (1999) thought that a glacial lake was a natural water body similar to artificial reservoir formed by ancient or modern glaciers. Additionally, there are many similar definitions of glacial lake: (1) glacial lake is one kind of lake formed by glaciation or supplied by glacial meltwater (Cao et al., 2016); (2) glacial lake is plateau lake located at the terminus or the lateral part of one glacier and glacier provides water resource when it retreats or melts (Tang et al., 2014); (3) glacial lakes were located in a basin formed by alpine glacier movement since the Last Glaciation Maximum (LGM), and their water were mainly from modern glacial meltwater or atmospheric precipitation (Chen et al., 2015). In these definitions of glacial lake above, it is emphasized that glacial lakes are formed by glaciation. The main differences are whether the time information being given or not and the material source of glacial lakes. The discrepancy of accepted concept of glacial lake caused that it was difficult to separate glacial lakes from natural lakes, which was particularly obvious in the inventory of glacial lakes and the change researches afterwards. Instead, the lakes in a specified distance of glaciers were treated as glacial lakes by scholars. For instance, Wang et al. (2013) and Zhang et al. (2015) selected lakes in a 10 km buffer of glaciers as glacial lakes in the Tianshan Mountains and Tibetan Plateau. However, it is debatable that these lakes selected are whether supplied by glacial meltwater or there are other lakes formed by glaciation beyond the specified extent. Undoubtedly, glacial meltwater is the main material source of glacial lakes as these definitions above mentioned. But it is very difficult to quantify the glacial meltwater and calculate its proportion in lake water volume in reality. If one lake receives glacial meltwater but is distant from the glacier, or the glacier supplied lake disappears, it is also one problem to judge whether these lakes are glacial lakes or not. These challenges had been causing that some research results of glacial lakes were not comparable; meanwhile, it was also difficult to share dataset of glacial lakes among different organizations.


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 2 Glacial lake
Type: text

or there are other lakes formed by glaciation beyond the specified extent . Undoubtedly , glacial meltwater is the main material source of glacial lakes as these definitions above mentioned . But it is very difficult to quantify the glacial meltwater and calculate its proportion in lake water volume in reality . If one lake receives glacial meltwater but is distant from the glacier , or the glacier supplied lake disappears , it is also one problem to judge whether these lakes are glacial lakes or not . These challenges had been causing that some research results of glacial lakes were not comparable ; meanwhile , it was also difficult to share dataset of glacial lakes among different organizations .

From the mechanism of glacial lake formation, glacial lakes are generated in erosion depressions caused by glaciers advance and retreat, and then receive glacial meltwater and precipitation. That is to say, glaciation is the dominant factor in the formation of glacial lakes. The LGM is the latest glaciation period when glacier coverage was the largest on the Earth. Theoretically, a glacial lake can be defined as the natural water body formed in the depression by glaciation since the LGM. However, there are still many problems and controversies in this definition of glacial lake. One thorny issue is the determination of the coverage extent of glaciers in the LGM. The other is whether the great lakes over the Tibetan Plateau such as Namco, Yamzho Yumco Lake are glacial lakes or not. Recently, the studies of glacial lake were mainly focused on the following fields: the inventory of glacial lakes based on remote sensing images, the response of glacial lakes to modern climate change, the coupling relationship between glacier variation and glacial lake evolution, the identification of dangerous glacial lakes and glacial lake outburst flood/debris flow disasters, etc. On the one hand, the focus of these studies is modern evolution processes of glacial lakes in the temporal dimension, and on the other hand they all emphasized the role of modern glaciers on the formation and evolution of glacial lakes. Therefore, in view of the modern process and practical application, glacial lakes can be defined as natural water mainly supplied by

modern glacial meltwater or formed in glacier moraine's depression. In the latter definition of glacial lake, it is emphasized on the role of modern glaciers in the formation and change of glacial lakes. For the inventory or digitalization of glacial lakes in one region, it is suggested that the glacier dataset including the WGI, the First and Second Chinese Glacier Inventory can be selected as the foundation of modern glaciers. To be sure, the classification system of glacial lakes in the next section is based on the latter definition of glacial lake.


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake
Type: text

Internationally, there has been so far no accepted standard for the classification system of glacial lakes. Some organizations and scholars proposed the different classification systems of glacial lakes according to their own research purposes. In the inventory of glacial lakes in the Hindu Kush-Himalayan region, ICIMOD (Pradeep et al., 2001) divided the glacial lakes into 5 classes: glacial erosion lake, moraine-dammed lake, ice-blocked lake, supraglacial lake and subglacial lake. Based on this classification schema, Wu et al. (2011) proposed a detailed classification system including 3 classes and 10 subclasses. In the study of glacial lakes in the Altay Mountains, Yi and Cui (1994) suggested a multiple classification schema. According to the mechanical and thermal differences in the formation of glacial lakes, they were classified as glacial erosion lake, ice-blocked lake, moraine-dammed lake, glacial thaw lake and glacial composite lake. According to the water supply of glacial lakes, they were divided into ice-water lake and non-ice-water lake; the former were mainly supplied by glacial meltwater and the latter were dominated by surface runoff from atmospheric precipitation. In the inventory of glacial lakes in the Chinese Himalayas, Wang et al. (2010) presented a classification system including moraine-dammed lake, ice-blocked lake, cirque lake, glacial erosion lake, landslide-dammed lake, supraglacial lake and glacial valley lake. Cao et al. (2016) adopted a classification system including 3 classes and 6 subclasses: glacial erosion lake (including cirque lake and other glacial erosion lake), moraine-dammed lake (including end moraine-dammed lake, lateral moraine-dammed lake and other moraine-dammed lake) and supraglacial lake. Additionally, Wang et al. (2016) proposed an integrated classification schema of glacial lakes according to the formation age of glacial lakes, the properties of dam, the shape of lake basin, the water supply, the area change, the spatial relation between glacial lake and its supply glacier, the risk level, etc. But there was a defect of disagreement between the actual glacial lakes classification and the theoretical classification system. On the basis of previous researches and the principles of systematic, normalization, operability and scalability, we proposed a complete classification schema of glacial lakes including 6 classes: glacial erosion lake, moraine-dammed lake, ice-blocked lake, supraglacial lake, subglacial lake and other glacial lake (Table 1). According to the position or geomorphologic characteristics of glacial lakes, the first 3 classes can be divided into 8 subclasses; cirque lake, glacial valley lake, other glacial erosion lake, end moraine-dammed lake, lateral moraine-dammed lake, moraine thaw lake, advancing glacier-blocked lake and other glacier-blocked lake.

Due to the wide application of satellite remote sensing images in the inventory of glacial lakes and studies of their changes, the interpretation characteristics of different types of glacial lakes are given in the following. Before the interpretation of glacial lake types, it is noted that researchers should firstly determine the approximate distribution of glacial lakes based on the inventory of glaciers and digital elevation model (DEM) data; that is to say,


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake
Type: text

; cirque lake , glacial valley lake , other glacial erosion lake , end moraine - dammed lake , lateral moraine - dammed lake , moraine thaw lake , advancing glacier - blocked lake and other glacier - blocked lake . Due to the wide application of satellite remote sensing images in the inventory of glacial lakes and studies of their changes , the interpretation characteristics of different types of glacial lakes are given in the following . Before the interpretation of glacial lake types , it is noted that researchers should firstly determine the approximate distribution of glacial lakes based on the inventory of glaciers and digital elevation model ( DEM ) data ; that is to say ,

lakes are only considered in the basin of modern glaciers. Meanwhile, although the lake is easily identified because of the obvious differences in the spectral characteristics of water body and its surrounding features in remote sensing images, it is very difficult to distinguish the types of glacial lakes scarcely by a single sensor or mono-phase remote sensing image. So other data sources and technologies should be needed, too. For instance, DEM data can be used to produce the mountain shadow so as to remove the wrong interpretation of glacial lakes; 3D visualization technology such as the Google Earth and high-resolution remote sensing images can be integrated to distinguish the type of glacial lakes.


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake
Type: table
Table

| Table 1 | The classification | system of | glacial lakes |
|---------|--------------------|-----------|---------------|
|---------|--------------------|-----------|---------------|

Table 1
The classification
system of
glacial lakes


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake
Type: table
Table

| Class                   | Subclass                       | Description                                                                          |
|-------------------------|--------------------------------|--------------------------------------------------------------------------------------|
| Glacial erosion lake | Cirque lake                    | The lake in one cirque                                                               |
|                         | Glacial valley lake            | The lake in U-shaped valley by glaciation                                            |
|                         | Other glacial erosion lake     | The lake formed by glacier erosion but not belonged to other classes of glacial lake |
| Moraine-dammed lake  | End moraine-dammed lake        | The lake between the end moraine ridge and glacier terminus                          |
|                         | Lateral moraine-dammed lake    | The lake beside the lateral moraine ridge                                            |
|                         | Moraine thaw lake              | The lake on the moraine ridge                                                        |
| Ice-blocked lake        | Advancing glacier-blocked lake | The lake blocked by advancing glacier                                                |
|                         | Other glacier-blocked lake     | The lake with the dam being glacier ice                                              |
| Supraglacial lake       |                                | The lake on the surface of glacier                                                   |
| Subglacial lake         |                                | The lake within the glacier or over the glacier bed                                  |
| Other glacial lake      |                                | The lake blocked by landslide, avalanche, debris flow, etc.                          |


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake > 3.1 Glacial erosion lake
Type: text

Glacial erosion lake refers to the water body in the depression formed by erosion and abrasion of glacier in the process of glacier movement. At present, the common glacial erosion lakes were mostly formed by erosion of Quaternary glaciers. These glacial lakes were mainly supplied by atmospheric precipitation and had few modern glaciers in their upper part. As mentioned above, if no modern glaciers exist in the upper lakes, these lakes will not belong to glacial lakes. That is to say, ancient glacial erosion lakes should not be considered. According to the location and geomorphologic characteristics of glacial erosion lakes, they can be divided into the following 3 subclasses.

3 Classification system of glacial lake
3.1 Glacial erosion lake


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake > 3.1 Glacial erosion lake > 3.1.1 Cirque lake
Type: text

The outstanding feature of the cirque lake is that it is located in the cirque. The typical cirque is like an arm-chair surrounded by three steep rock walls and a high reverse rock sill (see Figure 1, the centroid coordinates of the lake are 29°42'18"N, 96°18'32"E). Cirque lakes are usually small and near the equilibrium-line altitude (ELA) of glaciers. In the identification of cirque lake based on remote sensing images, it is necessary to use slope data derived from DEM data or Google Earth software to check the consistency between terrain features of glacial lakes and the cirque. If its terrain features satisfy the shape of the cirque, glacial lakes can be classified into cirque lake.

3 Classification system of glacial lake
3.1 Glacial erosion lake
3.1.1 Cirque lake


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake > 3.1 Glacial erosion lake > 3.1.2 Glacial valley lake
Type: text

Large glaciers usually formed a U-shaped valley being steep and straight on both sides and

flat at the bottom by laterally eroding in tributary and downward eroding on the glacier bed. Glacial meltwater and atmospheric precipitation flowed into the valley and then constituted a lake, namely glacial valley lake. This kind of glacial lakes were usually larger and far from modern glaciers. In order to expediently distinguish with moraine-dammed lakes since the Little Ice Age (LIA) and consider their potential danger on the downstream areas, some lakes in alpine region could been classified into glacial valley lake. According to the distance from the Akkol Lake to its

3 Classification system of glacial lake
3.1 Glacial erosion lake
3.1.2 Glacial valley lake


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake > 3.1 Glacial erosion lake > 3.1.2 Glacial valley lake
Type: figure
Figure

Image /page/5/Picture/3 description: A Google Earth satellite image displays a rugged, snow-covered mountain range. In the center of the image, a dark blue body of water is outlined in yellow and identified by a label in the bottom-left corner as a "Cirque lake." The lake is situated in a large, bowl-shaped valley, or cirque, surrounded by steep, icy mountain peaks and glaciers.


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake > 3.1 Glacial erosion lake > 3.1.2 Glacial valley lake
Type: figure
Figure: Figure 1 Cirque lake

Figure 1 Cirque lake


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake > 3.1 Glacial erosion lake > 3.1.2 Glacial valley lake
Type: text

supply glacier (Kanas Glacier) in the Altay Mountains and the area of Kanas Glacier (Figure 2), it is suggested that the identification of glacial valley lake can be based on the following criteria: (1) the morphology of lake should be U shaped; (2) there should be large glacier with an area of above 20 km2 in the upper lake; (3) the distance from lake to its supply glacier should be less than 15 km so as to distinguish with tectonic lakes.


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake > 3.1 Glacial erosion lake > 3.1.2 Glacial valley lake
Type: figure
Figure

Image /page/5/Figure/6 description: A Landsat TM, 5-4-3 band combination satellite image of a mountainous, glaciated region. The image is framed by latitude and longitude coordinates, with latitude ranging from 49°0'N to 49°10'N and longitude from 87°30'E to 87°50'E. The landscape is dominated by light blue glaciers and snow, with exposed mountain ridges appearing in reddish-brown and green hues. Two key features are labeled: 'Akkol Lake', a dark blue body of water on the left, and 'Kanas Glacier', a large glacier on the right. A scale bar in the bottom right corner indicates a length of 4 km.


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake > 3.1 Glacial erosion lake > 3.1.2 Glacial valley lake
Type: figure
Figure: Figure 2 Glacial valley lake

Figure 2 Glacial valley lake


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake > 3.1 Glacial erosion lake > 3.1.3 Other glacial erosion lake
Type: text

Glacial lakes formed by glacier erosion, which are difficult to be identified by morphological features and do not belong to the other types describing in the flowing, can be classified as other glacial erosion lake.

3 Classification system of glacial lake
3.1 Glacial erosion lake
3.1.3 Other glacial erosion lake


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake > 3.2 Moraine-dammed lake
Type: text

Moraine-dammed lake is a water body between moraine ridge and glacier due to the ob-

struction of moraine ridge. According to the location, moraine-dammed lakes can be divided into 3 subclasses: end moraine-dammed lake, lateral moraine-dammed lake and moraine thaw lake

3 Classification system of glacial lake
3.2 Moraine-dammed lake


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake > 3.2 Moraine-dammed lake > 3.2.1 End moraine-dammed lake
Type: text

When glacier retreats, glacial meltwater accumulated in the space between glacier terminus and end moraine ridge, then formed a lake, namely end moraine-dammed lake. This kind of moraine-dammed lake is the main type causing glacial lake outburst flood or debris flow in the Himalayas and Nyainqentanglha Mountains. End moraine-dammed lakes are usually easy to distinguish in the remote sensing image because they are mostly linked to glacier terminus or the distance between them is very small (Figure 3a). In addition, the end moraine ridge uplifting is clear in the Google Earth software (Figure 3b), which can help to identify the end moraine-dammed lake, too. In the downstream of larger glacier terminus, there are usually several end moraine ridges and some lakes are formed among them. These lakes are mainly supplied by glacial meltwater and are formed by glaciation, so they can be classified as end moraine-dammed lake. Based on the measurement among these lakes in the Himalayas, the distance between the upper and the lower end moraine-dammed lakes is less than 2.0 km. In other words, the lakes with a distance being far from the upper end moraine-dammed lake than 2.0 km should be not identified as end moraine-dammed lake.

3 Classification system of glacial lake
3.2 Moraine-dammed lake
3.2.1 End moraine-dammed lake


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake > 3.2 Moraine-dammed lake > 3.2.1 End moraine-dammed lake
Type: figure
Figure

Image /page/6/Figure/5 description: A side-by-side comparison of two satellite images of a glacial region, labeled (a) and (b). Both images show the same area, with latitude coordinates from 27°54'N to 27°58'N and longitude from approximately 88°3'E to 88°6'E. Panel (a) is a 'Landsat OLI, 5-4-3 band combination' image. It displays a mountainous landscape with white glaciers and two blue lakes outlined and identified by a legend as 'End moraine-dammed lake'. A scale bar indicates a length of 1 km. Panel (b) is a 'Google Earth' image, providing a more detailed, photographic view. In this image, the lakes appear icy and light blue. Arrows point to the 'End moraine', a ridge of glacial debris that dams the lower lake.


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake > 3.2 Moraine-dammed lake > 3.2.1 End moraine-dammed lake
Type: figure
Figure: Figure 3 End moraine-dammed lake

Figure 3 End moraine-dammed lake


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake > 3.2 Moraine-dammed lake > 3.2.2 Lateral moraine-dammed lake
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Lateral moraine-dammed lake refers to the water body beside the lateral moraine ridge of glacier. These glacial lakes usually appear around the larger valley glacier and are formed by main valley glacier blocking the meltwater of tributary valley glacier. For instance, there are 5 lateral moraine-dammed lakes beside the Ngozumpa Glacier which is the largest glacier in the southern Mount Everest (Figure 4), and the third Gokyo Lake with an area of 0.59 km2 is the biggest one.

3 Classification system of glacial lake
3.2 Moraine-dammed lake
3.2.2 Lateral moraine-dammed lake


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake > 3.2 Moraine-dammed lake > 3.2.3 Moraine thaw lake
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In the end/lateral moraine ridge of glacier, there are usually many but small ponds (Figure 5),

which are named as glacial thaw lake or moraine-dammed lake (Yi and Cui. 1994; Wang et al., 2016). The principal agent controlling these lakes is freeze-thaw process. For example, dead ice within the moraine ridge melts due to the temperature rising, and then the surface sinks and forms the depression retaining water. Although these ponds do not receive glacial meltwater but are formed by the dead ice meltwater of glacier, they can be classified as glacial lakes, namely moraine thaw lake. It should be pointed out that thaw lakes widely distributed in rock glacier and frozen ground region do not belong to moraine thaw lake, and they should be

3 Classification system of glacial lake
3.2 Moraine-dammed lake
3.2.3 Moraine thaw lake