Document: Crisis and Disaster Situations on Social Media Str
Section: EVENT EXTRACTION
Type: text

Event extraction from Twitter is carried out through the Twitter Streaming API, which is the standard application fulfilling the filtration process effectively (El-Halees, & Al-Asmar, 2017; Sakaki et al., 2010). It extracts the tweets for the event by crawling through the hashtags created for the events by various NEWS sources on Twitter and monitors the user-generated posts subsequently (Silva, Wuwongse, & Sharma, 2013). The crawler is started to fetch the tweets that have been monitored and stored in the data store. Each and every tweet consists of the original post, author, timestamp, geographic information and hashtag from which it was obtained. All these properties are very useful in deriving the patterns and identifying the purpose of the posts.

The formal definition for entity extraction of Twitter streams is expressed in the graph theory as G(E,V) where E and V represent some set of edges for the given set of vertices. To determine the potential social entities prevailed in the twitter streams and build the appropriate relationships between the entities, we link the set of edges E, in which  $v_i \in V$ , i = 1...N denotes the extracted entities in twitter streams and  $v_iv_j \in E$  denotes relationship between entities  $v_i, v_j \in V$ . In this connection, to estimate the candidate entity for the query q, the search engine would normally generate most ambiguous entity sets about the given candidate entity, and it is termed as

$$a_i = q \leftarrow a_i \tag{1}$$

However, in our proposed approach, we have introduced a novel method to tackle this ambiguity prevailed over the search results by incurring the semantic web ontology for the domain at which the entity is dealt with through the appropriate level of ontological weight, and it can drastically reduce with the addition of ontology candidate keyword KW, i.e. consequent of

$$aw_i = q \leftarrow a_i, KW$$
 (2)

which reduced the entity ambiguity with which  $|aw_i| \le |a_i|$ ,  $|a_i| \in a_i$  is a cardinality of  $a_i$  and  $|aw_i| \in aw_i$  is a cardinality of  $a_i$ , KW. By utilizing a well-formed query for the candidate entity in the query, the named entity information would come as

$$a_{"i"} = q \leftarrow "a_i" \tag{3}$$

In some cases,  $|a_{il}| \le |a_i|$  and  $|a_{il}| \in a_{il}$  is a cardinality of "a;". As well as with

$$aw_{i''} = q \leftarrow \text{"a}_i\text{"}, KW \tag{4}$$

is about one of the information concentrations of a named entity. Then after pruning the entity cardinality, in the next process, the relationship between two named entities is based on the concept of co-occurrence. Thus,

$$a_i \ a_i = q \ \leftarrow a_i, a_i \tag{5}$$


Document: Crisis and Disaster Situations on Social Media Str
Section: EVENT EXTRACTION
Type: text

a_ { il } | \ in a_ { il } $ is a cardinality of " a ; " . As well as with $ $ aw_ { i ' ' } = q \ leftarrow \ text { " a } _i \ text { " } , KW \ tag { 4 } $ $ is about one of the information concentrations of a named entity . Then after pruning the entity cardinality , in the next process , the relationship between two named entities is based on the concept of co - occurrence . Thus , $ $ a_i \ a_i = q \ \ leftarrow a_i , a_i \ tag { 5 } $ $

which is a process to augment the semantic similarity between the two named entities and build the relationships between them, with which  $|a_i \cap a_j| \le |a_i|$  and  $|a_i \cap a_j| \le |a_j|$  and  $|a_i \cap a_j| \in a_i a_j$  is a cardinality of  $a_i a_j$ . Besides, with the supplement of a keyword towards the co-occurrence will usually subside the number of entities given, and that is

$$aw_i aw_i = q \leftarrow qa_i, KW$$
 (6)

But it should satisfy that  $|aw_i \cap aw_j| \le |a_i \cap a_j|$ ,  $|aw_i \cap aw_j| \in aw_i aw_j$  is a cardinality of  $a_i a_j$ , KW. Similarly, effective utilization of the well-defined entity set for the query will yield the appropriate relationships between the two named entities.


Document: Crisis and Disaster Situations on Social Media Str
Section: ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT
Type: text

Semantic technologies that support crisis events identification are often required for interacting between different developers and software applications operated by various agencies. In this context, affecting the semantically enabled system to communicate the information in a unified format is the critical challenge, and social media platforms behave differently to address the dimension of the problem to interrelate with one another. Interoperability shortcomings at the semantic level of concepts can be alleviated using common vocabularies as well as shared concepts in linking the whole processes (Liu, Brewster, & Shaw, 2013; Rajpathak & De, 2016). The best way of accomplishing this critical task is through the use of machine-understandable ontologies that can precisely define the concepts, categorize the events based on clustering approach and build an appropriate path between concepts and unified communication.

The next aspect of the challenge in retrieving the crisis-related information is in extracting related events or messages from blogs, forums, and referring wikis. These are the places where vibrant information is present at a high rate and shared opinions and suggestion made by several authors. Now the critical challenge is interlinking not only social media platforms but also these blogs and forums. It is very difficult in repurposing the content and tough to identify the common events among these sites. For instance, take Wikipedia – it is a huge repository of publicly accessible knowledge source but reusing the same knowledge for other applications presents new challenges and difficulties (Kumar & Muruganantham, 2016; Yates & Paquette, 2011). Furthermore, a user can create accounts on many sites like in blogs, forums, wikis, and other social media platforms, but it is very complicated to inter-relate the candidate entities among these different social sites. The major problem pertaining to these media sources is that the information items in such sites are entirely disconnected and completely separated from one another. There is an absolute lack of exchanging the semantics of entities and unable to derive the facts from such information silos. Every site holds the information posted by their registered users independently and, at times, it has turned into stagnant information silos which are untapped by others.

To meet the challenges rising from crisis or havoc situations, there would be a huge demand to decentralize the process and enable interactive processes to fetch hidden relevant facts from the content. Table 4 gives the details of handling the events from the time news originates to planning for future action taking against the crisis events (i.e., past to present).


Document: Crisis and Disaster Situations on Social Media Str
Section: ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT
Type: table
Table

|                         | News Inception                              | Present State                 | Future Plan                |
|-------------------------|---------------------------------------------|-------------------------------|----------------------------|
| Goal                    | Inform & Publish the news                   | Share & Collect the news      | Engage & Prune             |
| Main Activity           | Gather relevant event- based information | Track & Monitor the events | Prepare the action         |
| Content                 | Initially, discrete data                    | Clustered Data                | Find Relationships         |
| Information Handling | Confidential                                | Privileged Access             | Absolute transpar- ency |
| Software Tools          | In-house Software                           | Commercial Software           | Open Source Software    |

News Inception
Present State
Future Plan


Document: Crisis and Disaster Situations on Social Media Str
Section: ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT
Type: text

Table 4. Information handling for crisis situations

Semantic Web recently provided the necessary tools for effective information linking and interoperability. Moreover, many semantic Web vocabularies have successfully been deployed at various social platforms facilitating machine-understandable message processing (Benali, & Rahal, 2017; Ritter et al., 2012). Some of the semantic Web vocabularies are RSS, FOAF (Friend of a Friend), and SIOC (Semantically Interlinked Online Communities). With the help of these and other more refined semantic vocabularies, interlinking communities and social sites became effective and helped curtail down information redundancy.

For example, let's consider the query for crisis-related content on Twitter such as "Was there a storm near the city?" In this query, the name of the city is not mentioned, but the tagging engine like DBPedia Spotlight (http://wiki.dbpedia.org/projects/dbpedia-spotlight) and OpenCalais (http://www.opencalais.com/) would annotate the given query and fix the appropriate entity for the annotated tokens based on the Agglomerative Clustering techniques applied on the collected tweets. Since there is a semantic link (i.e., rdf:type) between the query and DBPedia, it can be computed based on the similarity score and higher relevance of content. It is illustrated in the following:


Document: Crisis and Disaster Situations on Social Media Str
Section: ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT
Type: figure
Figure

Image /page/9/Figure/6 description: A diagram illustrates a query and result relationship using DBpedia entities. The 'Query: Was there storm near the city?' points with a dashed arrow to a circle labeled 'Dbpedia-owl: City'. Below, the 'Result: There was heavy storm near New York City yesterday.' points with a dashed arrow to a circle labeled 'Dbpedia-city: New York City'. The two circles are connected by a double-headed arrow labeled 'rdf:type', indicating that 'Dbpedia-city: New York City' is a type of 'Dbpedia-owl: City'.


Document: Crisis and Disaster Situations on Social Media Str
Section: ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT > Ontology Languages
Type: text

The so-called 'semantic Web stack' comprising a number of semantic Web languages was suggested for effective utilization in information processing and that, by the way, leads to efficient semantic implementations on the retrieval systems. The first language that was suggested to use was the Resource Description Framework (RDF), by which the basic framework to represent the potential information on the Web content was availed. The basic structure of any RDF statements is just a triple (subject, predicate, and object), which further yields the hierarchical RDF graph to prune the data in a much effective way. In simpler terms, RDF statement is just denoting the relationship existing between the existing things called nodes and that node is interconnected with other nodes (i.e., semantically related nodes). RDF serialization uses XML syntax and terms such as element name, attributes, values, etc. The scope of using RDF is to make the system machine-readable and process the infor-

mation semantically (see Table 5). Besides, it integrates the data without any serious glitches as it follows the well-formed logic which is universally acknowledged.


Document: Crisis and Disaster Situations on Social Media Str
Section: ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT > Ontology Languages
Type: table
Table: Table 5. Semantic Web languages and their structural contents

| SW Languages | Structural Contents                              |
|--------------|--------------------------------------------------|
| OWL-S        | Services in a machine-understandable format      |
| SWSL         |                                                  |
| WSML         |                                                  |
| OWL          | Equality                                         |
|              | Relation between Classes                         |
|              | Cardinality Restrictions for Properties          |
|              | Relation of Properties                           |
| RDF Schema   | Classes                                          |
|              | Objects                                          |
|              | Properties                                       |
| RDF          | Prepositions as Triplets                         |
| WSDL         | Services in an exact human understandable format |


Document: Crisis and Disaster Situations on Social Media Str
Section: ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT > Ontology Languages
Type: text

A set of RDF statements form an RDF graph through interconnected nodes. As the RDF graph is conventionally followed in expressing the logical facts about the potential named entities, ontologies are used to give the domain and category of each thing (i.e., entity) and yield the appropriate relationship between them. Ontologies contain features to wholesomely express the rich relationship between entities and also set appropriate constraints on them (Grolinger et al., 2011; Wang et al., 2018). The language followed for effective creation of ontology is RDF Schema and OWL (see Tables 6 and 7). The RDF Schema (RDFS for short) employs the set of classes related to the entity and chooses the properties according to its domain.

The basic objective of RDFS is to provide a well-structured description of entities properties. Some ontologies used to set classes and properties are called RDF vocabularies, and examples include FOAF, SKOS, MOAC, Dublin Core, etc., whereas OWL is facilitating the information interoperability and providing additional vocabularies to enhance the formal semantics of RDF Schema.


Document: Crisis and Disaster Situations on Social Media Str
Section: ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT > Ontology Languages
Type: table
Table: Table 6. RDF and OWL Ontological Constructs

| RDF/OWL  | Category   | Functions             |
|----------|------------|-----------------------|
| Class    | Definition | Class                 |
|          |            | Enumerated Class      |
|          |            | Restriction           |
|          |            | IntersectionOf        |
|          |            | UnionOf, ComplementOf |
|          | Axiom      | subClassOf            |
|          |            | Equality              |
|          |            | disjointWith          |
| Relation | Definition | Property              |
|          |            | Domain, range         |
|          |            | subPropertyOf         |
|          | Axiom      | (Inverse)Functional   |


Document: Crisis and Disaster Situations on Social Media Str
Section: ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT > Ontology Languages
Type: table
Table

| RDF/OWL  | Category   | Functions                                    |
|----------|------------|----------------------------------------------|
|          |            | Equality, inverseOf Transitive, symmetric |
| Instance | Definition | Type                                         |
|          | Axiom      | (In)Equality                                 |


Document: Crisis and Disaster Situations on Social Media Str
Section: ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT > ONTOLOGY EDITORS
Type: text

In order to derive the meaning out of the collected information from Twitter streams and as we process the tweets into the respective semantic representations, there is a need to design an ontology that is well mapped to the information and facilitates fetching the content in the hierarchical format (Liu, Shaw, & Brewster, 2013; Malizia et al., 2010). In this connection, we are required to build ontology using standard text editors that are very simple in design and development. Besides, it can be formatted with the semantic Web languages called RDF-XML format. One such editor is called Protégé (https://protege.stanford.edu/) which is a tool that permits designing OWL ontologies and further helps to connect to the data interrelated with one another in the overall ontological framework. With Protégé, querying and reasoning help to disambiguate the information and assist in making the filtration absolutely error-free. Other popular ontology editors are SWOOP, OntoStudio, NeOn, Altova, WebODE, and so on. Among all the editors, Protégé was the first, freely available, and open-source ontology editor and framework for building intelligent systems thus tops the list and widely deployed in much recent research.


Document: Crisis and Disaster Situations on Social Media Str
Section: ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT > ONTOLOGY EDITORS
Type: table
Table: Table 7. A notional mapping between RDF/OWL and relational concepts

| RDF/OWL Terms | Relational Concepts                        |
|---------------|--------------------------------------------|
| rdf:class     | Table                                      |
| rdf:property  | Column                                     |
| rdfs:domain   | Table that the rdf:property is a column of |
| rdfs:range    | Data Type of the column                    |
| rdf:type      | Values of the Primary Key column           |


Document: Crisis and Disaster Situations on Social Media Str
Section: ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT > SEMANTIC MATCHING AND TRANSLATION
Type: text

As ontologies play a seminal role in semantic processing of information (Celik et al., 2011; Sheth et al., 2010), we should, therefore, try harnessing the potential meaning hidden in the collected information streams. Ontologies help process the meaning of different terms represented in the information and avail the system to understand the very basic structure of information in a precise way. The system processes the collected data automatically and does matching and translation with its rich vocabulary sets, which is fed as the dataset to the ontology while designing the complete framework of the domain ontology (Gruhl et al., 2009; Madani, Boussaid, & Zegour, 2015). Each term (i.e, a concept) in a tweet is mapped with corresponding vocabulary sets in the specific ontology domain, and sometimes it arches to other domains of vocabulary set to find the exact meaning of the concepts represented in the tweet. Precisely, the inclusion of mapping the terms over multiple ontologies is the biggest challenge in designing an application that is used to integrate the ontologies to disambiguate in parallel; that is a challenging research area to deal with. Further, to make the automation of semantic matching and translation effective, appropriate use of mapping rules over the information is necessary and should be defined using ontology matching tools. Ontology matching is variously called also as ontology aligning, mapping, and translation (for example, for Web services discovery: Fellah, Malki, & Elçi, 2016). Conventionally, ontology mapping tools come in two categories: element-based approach such as name similarity, entity similarity, concept similarity, etc., and structurebased approach such as sub/super -categories, -domains, -levels, etc. Besides, mapping requires infusing external knowledge such as Thesauri, WordNet, etc., to yield precision and high recall. Some of the ontology mapping tools available on the market are RiMOM, ASMOV, and AgreementMaker.


Document: Crisis and Disaster Situations on Social Media Str
Section: ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT > SEMANTIC SEARCH
Type: text

The next level in semantic utilization of social media harvested information is through semantic search, which should return results without any ambiguity and sparseness. As semantic mapping directly links information repositories, domain ontologies should facilitate the operation of effective semantic search (Kumar & Muruganantham, 2016) retrieving facts that are interconnected with one another in the ontological framework. In order to render the search process easier, appropriate use of indexing methods in the ontological inclusion over the concepts is deemed important. Ontologies follow the semantic indexing approach using its standard principle of "indexing the RDF triplets", thus smoothen the way of semantic search over the collected information. Several pieces of research have been carried out in this regard to fetch the precise and unambiguous results by semantically integrating information from diverse ontological frameworks in retrieving the results from multiple repositories.


Document: Crisis and Disaster Situations on Social Media Str
Section: ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT > INTEGRATION OF DATA
Type: text

Another critical issue faced in utilizing social media content is the integration of data, which may be considered from two different perspectives: data source (database/stream) reconciliation, and information integration. The basic objective of overcoming the problems of semantic heterogeneity between these two categories using the appropriate ontology framework is a challenging task to any research (Liu, Shaw, & Brewster, 2013). In considering database integration, the role of ontologies lies on the upper layer of the schema (i.e, semantic matching of information and table schema should be shared with the domain ontology and additionally, make use of the ontology to integrate the database schema rightly towards its order). While integrating information the core problem lies at integrating the terms from various sources and mapping the potential candidate terms relevant to its vocabulary sets and bring into the consolidated view called the new collection of a derived set. The challenge that lies here is to not change the original sense of the terms while mapping to the appropriate sense of terms in the vocabulary sets. Besides, in database integration, ontologies convert tables into respective classes in RDF triplet and columns in the table into data relation in the RDF Schema. The data models followed in the semantic conversion would always be 1:1 mapping cardinalities. In recent works (Kumar & Muruganantham, 2016; Kwak, Lee, Park, & Moon, 2010), the authors propose rules to dynamically map the data models into ontologies and consider mapping instances to class levels. Also, some language constructs are given to fetch data objects and, using the queries, they can be annotated dynamically representing in RDF.

Whereas in information integration, accumulating the terms from various sources of datasets to bring them into a unified collection, several efforts were carried out in the recent past but failed to resolve it. Some early researchers have tried to apply the Description Logic (DL) as ontology language and observed few changes in the outcome. Later, the Prolog programming language is employed for expressing the information formally and integrating the terms using appropriate domain ontology.

In the next section, we introduce our proposed information-centric model for management of crisis and disaster based situations through integration of many of the technologies mentioned above combined by our innovative approach. Our proposed model is introduced, followed by the empirical tests and discussion of findings.


Document: Crisis and Disaster Situations on Social Media Str
Section: THE PROPOSED MODEL
Type: text

The objective of this research is to potentially harness the information gathered from various social media platforms and render it relevantly interconnected with the selected news articles. In doing so,

here we introduce some of the notation and problems that we define formally before presenting the Semantic Search for Events Algorithm.

**Problem 1 (News stream):** For every news article related to disaster or crisis situation, content must be analyzed and scrutinized for a further level of comprehension. Let  $N = \{n_0, n_1, ..., n_i\}$  be the news posted on various sites and gathered from various news agencies. For every news article posted say  $n_i$ , we find the actually published time t ( $n_i$ ). Since the origin of news story gives the real arrival of news, it brings in the proximity among related news articles.

**Problem 2 (Tweet stream):** Upon the arrival of every news article related to disaster or crisis, the next task of the system is to identify the equivalent social media content such as Twitter where the relevant news item is discussed and promulgated. Let  $S = \{s_0, s_1, ..., s_j\}$  be the Twitter Streams for the taken news articles and load the inter-related Twitter messages posted by various potential social users. For every tweet  $s_i$ , we find the actually posted time t ( $s_i$ ) and responses for the message.

**Problem 3 (News recommendation problem):** Once the news items N and its associated social media contents (Twitter Streams) S are mapped, then the real task is to find the top-k most relevant news for the topic. Let's take the set of users interacted on the particular news topic  $U = \{u_0, u_1, u_2, ..., u_n\}$  in the social media platforms and explicitly categorize the social messages and news streams of general interest (i.e., for any social user  $u \in U$  at any point of time T, we recursively adopt the functional ranking which links the users interest among its neighbors).

**Problem 4 (Social influence):** In order to find whether the news item  $n_i \in N$  has influenced the social media users  $U = \{u_0, u_1, u_2, ..., u_n\}$  effectively, we give the social influence model  $S = |U| \times |U|$  matrix where S (i, j) calculates the cumulative interest of the selected users  $u_i$  to the usergenerated content by  $u_j$ . This process states that each user in the context would pose an absolute interest to the user-generated content posted by the other user.

**Problem 5 (Tweets-to-news model):** To merge the process, let N be the order of news collected and S be the streams of social media messages, we model the relationships between user-generated content and news items as  $M = |S| \cdot |N|$  matrix Z where S(i,j) is the closest proximity of user-generated content  $s_i$  to news item  $n_i$ .

```
Algorithm (Semantic Search for Events)

Input: Seed words for each crisis event

Output: Generation of semantic classes

BaseTerms ← set of seed words given;

for i: 1 to N (Number of Iterations) do

BaseTerms ← ExpansionOf (Seed words, Corpus);

BaseTerms ← Cluster (BaseTerms, Seed words, Corpus);

end

return BaseTerms
```


Document: Crisis and Disaster Situations on Social Media Str
Section: THE PROPOSED MODEL
Type: text

user - generated content and news items as $ M = | S | \ cdot | N | $ matrix Z where S ( i , j ) is the closest proximity of user - generated content $ s_i $ to news item $ n_i $ . ` ` ` Algorithm ( Semantic Search for Events ) Input : Seed words for each crisis event Output : Generation of semantic classes BaseTerms ← set of seed words given ; for i : 1 to N ( Number of Iterations ) do BaseTerms ← ExpansionOf ( Seed words , Corpus ) ; BaseTerms ← Cluster ( BaseTerms , Seed words , Corpus ) ; end return BaseTerms ` ` `

The algorithm carries two significant operations: (1) expand the seed words with the assistance of ontologies; and (2) cluster the events based on the similarities existing in the classification. For the given seed words for the event, it crawls for new terms that possess the similar distributional features to the seed word and assigned to the set of seed words (also called as candidate words). In fact, extracting the new terms for the seed word can be done based on the contextual features and top score similarity measure. For the clustering, the selection procedure would process the learned terms and

seed terms based on the distributed similarity and set the minimum threshold value for estimating the exact precision of the terms.

The sole plan of this algorithm is to get the patterns which are interlinked with a semantic relation and bring in the semantic class for the search terms (Kumar & Muruganantham, 2016; Wang et al., 2018). As listed in the algorithm, it has three core operations for finding the patterns existing in the disaster based situations:

- 1. using the semantic class expansion algorithm, extract the candidate terms for the disaster event tags;
- 2. find the patterns for the candidate terms selection and fix the semantic category of the events; and
- 3. choose the cluster events which hold similar action terms and evaluate the patterns for further classification.

It has been noted at several instances (Abel et al., 2011; Sheth et al., 2010; Wirtz et al., 2014) that news items and user-generated content at social media platforms (say, Twitter Streams) co-exist with one another with same news topic (see Figure 2). Sometimes, a published news story is pushed into social media platforms for further discussion and circulation. And, at many times, a news item first discussed vehemently on social networks then becomes a topic in news stories (Lei et al., 2014; Li, Liu, Li, Qin, 2016). In these two cases, the predominant factor is holding the current trending entities, which give the unflinching bond between social networks and news sites. There is an absolute relationship between user-generated content and news stories, which create an intermediary layer that paves the way to generalize the analysis. Hence, this would make our work equally applicable in deriving the ultimate decision for disaster management and assess the core patterns for decision making.

During the analysis of the relationship between events and results, there would emerge a need to attain a similarity score for the ultimate decision process. The similarity score for the crisis/disaster management (Liu, Shaw, & Brewster, 2013; Malizia et al., 2010; Schulz et al., 2013) can be accepted and formulated based on the following assumptions:


Document: Crisis and Disaster Situations on Social Media Str
Section: THE PROPOSED MODEL
Type: text

between user - generated content and news stories , which create an intermediary layer that paves the way to generalize the analysis . Hence , this would make our work equally applicable in deriving the ultimate decision for disaster management and assess the core patterns for decision making . During the analysis of the relationship between events and results , there would emerge a need to attain a similarity score for the ultimate decision process . The similarity score for the crisis / disaster management ( Liu , Shaw , & Brewster , 2013 ; Malizia et al . , 2010 ; Schulz et al . , 2013 ) can be accepted and formulated based on the following assumptions :

- 1. If there would be a high or low hazard during the disaster situations, then it requires the scientific or technical measure to be assured, and precautionary steps should be taken based on scientific or technical grounds.
- 2. If there would be high or low outrage, then it is an emotive issue and should be tackled through proper negotiations or political balance.

In these two cases, the analysis of the events played a crucial role in disseminating the user-generated content posted on social networks and determining the effective decision-making process (Heath & Bizer, 2011).


Document: Crisis and Disaster Situations on Social Media Str
Section: THE PROPOSED MODEL > DISASTER ONTOLOGY
Type: text

To substantiate our proposed model, we constructed an ontology for disaster datasets with a glossary consisting of more than 150 definitions (i.e. mostly of recurring terms) and further accumulated terms related to disaster from books, papers, survey on seismic risk and other relevant disaster web sites. We constructed the ontology with associated concepts, its attributes and proper relationships between concepts. In constructing this ontology, we followed many dictionary terms (i.e. entities related to disaster) with their associated meanings (axioms) and connected the terms with taxonomic relationships. Relationship mapping of terms can be done in many ways such as Taxonomic (IS-A relationship), Meronomic (PART-OF relationship) and Telic (PURPOSE-OF relationship). The relationship mapping of terms can be achieved through inference rules to augment better reasoning and increase the credibility ratio of knowledge representations. On this proposed system, we used Protégé, an ontology tool which is more of an object-oriented paradigm and well suited for term inher-

itances. The relationship IS-A is a generalization/specialization between the candidate entities: superclass entities publically generalize the subclass entities and the sub-class entities particularly employ specialization of superclass entities. Likewise, Protégé permits to formulate the disaster ontology by considering different instances to insert and able to accommodate a huge set of information for a digital archive.


Document: Crisis and Disaster Situations on Social Media Str
Section: THE PROPOSED MODEL > DISASTER ONTOLOGY
Type: figure
Figure

Image /page/15/Figure/2 description: A screenshot of a software interface displaying a flowchart related to risk assessment. The window has a menu bar with "File", "Tools", and "Help", and a toolbar with buttons like "Previous", "Next", and "Build graph". The main area is divided into two panes. The left pane contains a list of terms, with "Damage" highlighted in bold. The right pane shows a flowchart. At the center is a grey box labeled "Damage". An arrow flows from "Vulnerability" to "Damage", and another from "Damage" to "Exposure". The "Vulnerability" box is influenced by "Seismic Risk" and "Structural Vulnerability". The "Exposure" box is influenced by "Physics Elements Exposed" and in turn influences "Seismic Risk", "Functional Exposure", and "Strategic Exposure". Several boxes are marked with small numbered circles.


Document: Crisis and Disaster Situations on Social Media Str
Section: THE PROPOSED MODEL > DISASTER ONTOLOGY
Type: figure
Figure: Figure 2. Disaster Ontology using Protégé

Figure 2. Disaster Ontology using Protégé


Document: Crisis and Disaster Situations on Social Media Str
Section: THE PROPOSED MODEL > DISASTER ONTOLOGY
Type: text

Our proposed system concerns mostly about urban risk with specific governance on seismic risk management. The effective building of this ontology paves the way for common knowledge, makes the concepts understandable, and prompts information into unambiguous semantics. This ontology construction has been performed in three steps:

- 1. Fetch the core concepts of the domain (Seismic Risk) and relevant terms in the glossary.
- 2. Extract the Super-Classes and Sub-Classes of the concepts using the IS-A relationship.
- 3. Find other related types of relationships using inference rules (properties, slots, and roles associated with each concept).

Relation mapping for the collected tweets can be performed and filtered using the relational properties displayed in Table 8). Entity resolution and disambiguation have been effectively dealt with in Disaster Ontology constructed above and resolve the term ambiguity persisting over the collected documents (Twitter streams).


Document: Crisis and Disaster Situations on Social Media Str
Section: THE PROPOSED MODEL > DISASTER ONTOLOGY
Type: table
Table

| Relation Name    | Source     | Target      | Description                                                                                |
|------------------|------------|-------------|--------------------------------------------------------------------------------------------------------------------|
| isResponsibleFor | Department | Process     | Identify which sector is responsible for the event and map the relationship between de- partment and process |
| workIn           | Actor      | Department  | Map the relationship between the person and the department. Identify the actor responsible for the event.    |
| isPartOf         | Task       | Process     | Find the task which is responsible for the pro- cess and filter out the concepts related to the event.       |
| isA              | -          | -           | Relationship between super-class and sub-class                                                                     |
| Perform          | Actor      | Task        | Group the actor performed the task on the event.                                                                |
| Produce          | Task       | Information | Filter the information for the task on the event.                                                               |


Document: Crisis and Disaster Situations on Social Media Str
Section: THE PROPOSED MODEL > DISASTER ONTOLOGY
Type: text

Table 8. Relationship mapping between concepts and classes


Document: Crisis and Disaster Situations on Social Media Str
Section: THE PROPOSED MODEL > ENTITY RELATIONSHIP AND RANKING SCORE
Type: text

The disaster ontology has now become a knowledge source for our disambiguation effort. When we process each and every tweet, we find the exact match of those entities against the knowledge source such as DBpedia or YAGO. If it is not present, then it sends the NIL result. Now, by means of our proposed method, we can again cross-match with our own ontology created from news articles and find the exact match of those entities. In this method, the accuracy is relatively high because the created ontology is extracted from news articles related to the tweets and context of the news articles is highly relevant and appropriated match with the tweets. If we go for the entity-mention match with DBpedia, it lists out candidate mentions for the entity, and we need to probe for the context pertaining to the tweet. But if we match the same with our own ontology, it is exact and gives an appropriate match.

Hence, in our approach, we take the link probability (Kumar & Muruganantham, 2016; Yates & Paquette, 2011) for the entity with DBpedia mention, and it can be defined as follows:

$$F_{(e,m)} = \frac{Count(m,e)}{Count(m)} \tag{7}$$

Here, we utilized an outlined ontology to arrange the mentions for the given named entities and appropriately estimate the similarity distance between them. Now the task is to estimate the distance between the entity and the suggested set of mentions from DBpedia. In this connection, we have taken the Cosine Similarity measure to access the similarity difference existing between the entity and candidate mentions as follows:

$$CosSim(e,m) = \frac{Product(e,m)}{||e||*||m||}$$
(8)

By this method, we categorically filter the exact match of mention for the given entity and appropriately reference with DBpedia URI as stated in (Liu, Brewster, & Shaw, 2013; Malizia et al., 2010; Schulz et al., 2013). We utilized the DBpedia Spotlight to get the URI match of each entity and return the JSON results for our implementation.

The result of the proposed approach would create a binary mapping of the entity and mentions, as seen in Table 9.

THE PROPOSED MODEL
ENTITY RELATIONSHIP AND RANKING SCORE


Document: Crisis and Disaster Situations on Social Media Str
Section: THE PROPOSED MODEL > ENTITY RELATIONSHIP AND RANKING SCORE
Type: table
Table: Table 9. Identifying the relation between named entity and candidate mention

| Mention | NE Class | NE Link          | DBpedia Ontology Class | Score |
|-----------------------|----------------------------|--------------------------------|------------------------------------------|---------------------|
| Barack Obama   | Person              | Dbpedia: Obama, USA     | Dbpedia-owl: Person               | 3            |
| Chennai        | Location            | Dbpedia: Chennai, India | Dbpedia-owl: Place                | 1            |
| Cricket        | Sports              | Dbpedia: Cricket        | Dbpedia-owl: Sports               | 2            |

Mention
NE Class
NE Link
DBpedia Ontology Class
Score


Document: Crisis and Disaster Situations on Social Media Str
Section: THE PROPOSED MODEL > ENTITY RELATIONSHIP AND RANKING SCORE
Type: text

Generally, entities in DBpedia have its name, label, type, etc. and, to fetch the entity name given in the DBpedia for the specified URI, it can be queried through the SPARQL query as follows. For example, searching for 'Sachin Tendulkar':

```
Select distinct *
where {
?URI rdf:label ?name
?URI dbpprop:iupacname ?name
filter(str(?name) = "Sachin Tendulkar")
}
```

In order to get the category of a given entity from the DBpedia, we issue the following SPARQL query. For example, for 'Vehicle':

```
Select *
where {
    <http://dbpedia.org/resource/Vehicle>
    <http://purl.org/dc/terms/subject>
?categories.
}
```


Document: Crisis and Disaster Situations on Social Media Str
Section: EMPIRICAL TEST AND ANALYSIS
Type: text

We used Twitter4J API to gather disaster-related tweets from Twitter and utilized TextRazor API to effectively recognize the potential named entities present over the tweets and link them accordingly to its respective DBpedia URI. Additionally, we used the rich natural language processing tools of Stanford Core NLP Library to segregate tweet patterns and performed sentiment analysis for grasping the sense of the tweets. Tweets were collected on the month of August 2017 and, to witness the trust, we followed the leading news agencies on Twitter such as BBC World, CNN, New York Times, NDTV, and Breaking News. Tweets were crawled and stored only if they had at least one named entity that has its link on DBpedia URI. In our datasets, we were able to filter out 20 different topics and classified the tweets successively based on seismic risk by applying the classification rules. The algorithm proposed above is able to detect the factual information containing about 3 out of 5 tweets.


Document: Crisis and Disaster Situations on Social Media Str
Section: EMPIRICAL TEST AND ANALYSIS
Type: table
Table

| Event Category      | Total Events | Potential Sub-Events by Relevance |         |         |
|---------------------|-----------------|-----------------------------------|---------|---------|
|                     |                 | R3                                | R3+R2   | R3-R1   |
| Earthquake          | 75              | 35(46%)                           | 51(68%) | 59(78%) |
| Tsunami             | 120             | 46(38%)                           | 79(65%) | 88(73%) |
| Cyberattack         | 114             | 51(44%)                           | 87(76%) | 95(83%) |
| Unrest in a Country | 150             | 77(51%)                           | 90(60%) | 97(64%) |
| Celebrity Death     | 115             | 43(37%)                           | 66(57%) | 81(70%) |
| Terror Attack       | 120             | 68(56%)                           | 79(65%) | 85(70%) |

Event Category
Total Events
Potential Sub-Events by Relevance


Document: Crisis and Disaster Situations on Social Media Str
Section: EMPIRICAL TEST AND ANALYSIS
Type: text

Table 10. Event relevance and categories

We tested the DBpedia corpus to identify potential events on seismic risk, which provided the six complex event categories listed in Table 10. The entities were extracted based on the recommendations stated above and identified their relationship types in corresponding DBpedia URI. Besides, we again queried the DBpedia Knowledge Source for the sub-events correlated with the events extracted from the tweets. We substantially ranked the sub-events on the basis of frequency of occurrence and chose the best-matched event category to a tweet. After evaluating the event categories against DBpedia, we determined whether the event is of positive instance or not. Sometimes, the retrieved events would pose a challenging task such as if it is partially relevant but not exactly appropriate to the categorized concepts. During these anomalies, we assigned the following three relevance scores in order to fit the events into their appropriate decks:

- Relevance (R1): Events with fuzzy relationship to the concept/category.
- Relevance (R2): Events with positive occurrences of sub-events or subject-object mapping.
- Relevance (R3): Events are positive instances and fit into the category for the posted query.
- Otherwise, the relevance zero indicates the events with absolutely NIL relationship.

Table 10 displays detected event categories and potential sub-events or co-occurrence of events with relevance scores. As was witnessed, the precision values varied considerably among the categories. The Stanford NLP Library was deemed fit to extract the potentially relevant tweets, and type filtering of events was absolutely effective at identifying the appropriately named entities. We obtained an accuracy of 74.13% and computed the Precision (0.641), Recall (0.716) and F-Measure (0.691) respectively for the given datasets.


Document: Crisis and Disaster Situations on Social Media Str
Section: DISCUSSION
Type: text

The dynamic change in the amount of information gathered at the various medium of platforms indicates the need for a rapid decision-making process in crisis events. It was observed that the information gotten from these sources rapidly varied. Statistics (Wirtz et al., 2014) showed that the frequency of report variation grows ten times greater than the previous day. Besides, to better account for the report variation of the information accumulation, the report dimensions were categorized into three crucial breakpoints, i.e., D+1, D+5, D+10. This elapsed gap fetches the detailed overview of the crisis or disaster based events and showed us the real potential of the event happenings (see Figure 3).


Document: Crisis and Disaster Situations on Social Media Str
Section: DISCUSSION
Type: figure
Figure

Image /page/19/Figure/1 description: A line chart titled 'DAY WISE TWEETS' that plots the 'TWEET COUNTS' for four different events over a period of 10 days. The y-axis, 'TWEET COUNTS', ranges from 100 to 350,100. The x-axis shows the days, from DAY 1 to DAY 10. There are four lines representing four events: 'Event 1' (blue line with diamonds), 'Event 4' (yellow line with 'x' markers), 'Event 2' (orange line with squares), and 'Event 3' (gray line with triangles). Generally, Event 3 has the highest tweet count, followed by Event 2, Event 4, and finally Event 1 with the lowest count. All events show a significant peak in tweet counts on Day 4, with Event 3 reaching approximately 300,000, Event 2 reaching about 240,000, Event 4 at 180,000, and Event 1 at 90,000. Events 2 and 3 show another high peak on Day 10, reaching approximately 200,000 and 280,000 respectively, before all four events show a sharp drop in tweet counts at the very end of the period.


Document: Crisis and Disaster Situations on Social Media Str
Section: DISCUSSION
Type: figure
Figure: Figure 3. Daily frequency of information on social media platforms

Figure 3. Daily frequency of information on social media platforms


Document: Crisis and Disaster Situations on Social Media Str
Section: DISCUSSION
Type: text

Through the data obtained from various sources and on different days of report gathering, we can formulate deviance of patterns and get through the details of anomalies that exist in the report. By applying the pruning algorithm, we can sort the crisis events for the decision-making process and get to the core base of the events. In this research, the real task is to find the actual reason for the crisis event and get the substantiated evidence for its occurring. To augment this process, we classified the events into many chronological orders influenced by the usage of ontological background with semantic technologies. By mapping different day event reports, we scrutinize the process for discrimination (i.e., fetch the positive or negative or neutral feedback from the potential users on the social media) and allow filtering the facts based on cross-checking in tabulating the actual events of the situation.

Our approach achieved the accuracy rate of 74.13% where other existing models succeeded getting 68.42% using Support Vector Machine (SVM), 67.93% using Maximum Entropy Model (MEM), and 64.71% using Conditional Random Fields (CRF) based on the analysis successfully performed with the help of Table 9. Since our proposed model extensively uses the dedicated ontology of Crisis and Disaster, instead of employing the Bag-of-Words (BoW) method, we employed Bag-of-Concepts (BoC) and Relevance of Concepts, as well as calculating the semantic similarity score between ambiguous terms. Deep proliferation of the ontological network paved the way to yield the subcategories of a topic and skimmed the words that are completely unambiguous. The relevance R of the concepts were derived with other three relevances R1, R2, and R3 as shown in Table 10, whereas the other existing methods mostly used only a single relevance score and restricted the research scope to Bag-of-Words model.

The major contribution of this research is in collecting crisis-related temporal data from multiple bursty short-message sources and decision making through semantic mapping of entities over concepts disambiguating potential named entities. The problems persisting over entity ambiguity and its associated entity types were addressed as well. We categorized the disaster-based entity domains using ontology and enhanced the searching capability of the system by incrementing the explicit connection mutually existing between entity and an ontology class.


Document: Crisis and Disaster Situations on Social Media Str
Section: CONCLUSIONS
Type: text

In this paper, we proposed a novel solution to harvest and compare the content of Twitter streams and conventional news sources such as CNN, New York Times, BBC World, NDTV, and Breaking News in the cases of havoc situations. We developed a semantic filter that can map the concepts correlated between Twitter streams and traditional news sources, and can disambiguate the candidate entities based on the ontological framework particularly loaded with disaster/crisis events.

The major advantage of our work is that, instead of pruning a single news source, it paves the way for clustering the information from diverse sources and harnessing the potential information to derive the hidden facts in it. We also developed a disaster ontology for this research and used it to segregate the entities which pose ambiguity over other candidate sets.

Empirical results show that the approach based on our model outperforms other models available in the literature to solve this research gap by various other approaches. In the future, we shall strive to extend the model in order to help summarize and visualize the potential information ranked high by the model.


Document: D record Disaster Response and Relief Coordination Pipeline
Section: ABSTRACT
Type: text

We employ multi-modal data (i.e., unstructured text, gazetteers, and imagery) for location-centric demand/request matching in the context of disaster relief. After classifying the *Need* expressed in a tweet (the WHAT), we leverage OpenStreetMap to geolocate that *Need* on a computationally accessible map of the local terrain (the WHERE) populated with location features such as hospitals and housing. Further, our novel use of flood mapping based on satellite images of the affected area supports the elimination of candidate resources that are not accessible by road transportation. The resulting map-based visualization combines disaster-related tweets, imagery and pre-existing knowledge-base resources (gazetteers) to reduce decision-making latency and enhance resiliency by assisting individual decision-makers and first responders for relief effort coordination.


Document: D record Disaster Response and Relief Coordination Pipeline
Section: CCS CONCEPTS
Type: text

• **Information systems** → *Decision support systems*;


Document: D record Disaster Response and Relief Coordination Pipeline
Section: KEYWORDS
Type: text

disaster relief, location-centric processing, flood mapping, need matching


Document: D record Disaster Response and Relief Coordination Pipeline
Section: ACM Reference Format:
Type: text

Shruti Kar1\*, Hussein S. Al-Olimat1\*, Krishnaprasad Thirunarayan1, Valerie L. Shalin1, Amit Sheth1, Srinivasan Parthasarathy2. 2018. D-record: Disaster Response and Relief Coordination Pipeline. In *Proceedings of SIGSPATIAL Workshop (ARIC 2018)*, Jennifer B. Sartor, Theo D'Hondt, and Wolfgang De Meuter (Eds.). ACM, New York, NY, USA, Article 4, 4 pages. https://doi.org/10.475/123\_4


Document: D record Disaster Response and Relief Coordination Pipeline
Section: 1 INTRODUCTION
Type: text

Recent catastrophic events, e.g., resulting from floods and hurricanes, combined with increased urbanization and interdependent infrastructure reveal an increasing vulnerability to natural and human-made hazards. A resilience framework must accommodate both precise information for action and for various levels of analysis, posing substantial challenges [4]. These include characterizing hazards with an integrated ontology across voluminous, multi-modal

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

ARIC 2018, Nov. 2018, Seattle, Washington
© 2018 Copyright held by the owner/author(s).
ACM ISBN 123-4567-24-567/08/06.
https://doi.org/10.475/123\_4

data sources, facilitating rescue/response actions, and assessing the effects of pre- and post-disaster actions on individual needs.

The dire real-time need for integrated, coherent multi-modal data from different sources has motivated substantial research [8]. Social media is a significant source of such data. Twitter users, for example, post short texts as updates. These messages (a.k.a. tweets) reveal *Needs* and requires matching with possible available help. These messages can provide critical information to first responders and individual decision-makers to contribute effectively to relief efforts [18]. Tweets authored about crisis events may contain crucial but unstructured location-centric information (such as incident reports) even when explicit geo-tagging is absent. The ability to extract specific location information from this unstructured terminology and the other attached information is critical for timely assistance by the first responders.

Crowdsourced data (such as OpenStreetMap) that covers the geographical area of a disaster event can assist in the extraction of location content from tweets. To achieve this, we developed D-record: **D**isaster **re**sponse and **re**lief **coord**ination, a pipeline that utilizes multi-modal data (i.e., unstructured text, gazetteers, and satellite imagery). D-record allows first responders to visualize and match location-centric *Needs* and options for available help while taking into account transportation constraints and available options in the local area of a disaster.1


Document: D record Disaster Response and Relief Coordination Pipeline
Section: 2 RELATED WORK
Type: text

The following related work informs the D-record pipeline and clarifies its contributions

Disaster-Centric Ontologies. Lightweight lexicons or vocabularies like the Management of Crisis Vocabulary (MOAC)2, CrisisNLP3, and CrisisLex [21] provide the different concepts for disaster management but lack the granularity required for describing response and miss crucial spatial and thematic details. Other efforts like UNOCHA's Humanitarian eXchange Language (HXL)4 improve information sharing during disasters but lacks specific crisis-related concepts. The Social Media and Emergency Management (SMEM) ontology [13] combines emergency domain knowledge with social media information for Incident Classification. However, SMEM focuses on major coarse-grained categories of disasters such as meteorology or biology. As stated above, state-of-the-art techniques in event representation for disaster response is critically limited with respect to the usability and dissemination of recommended action and response (intentional intervention) required for relief

\*Authors contributed equally.

<sup>1The source code of the pipeline is available at https://github.com/shrutikar/d-record.

<sup>2http://observedchange.com/moac/ns/

3http://crisisnlp.qcri.org/

4http://hxlstandard.org/

efforts coordination. Given the above limitations, D-record uses a location-centric ontology to assist in matching relevant needs with available help to support a disaster response use-case as discussed in Section 3.

**Need Classification.** For extracting the type of *Needs* (e.g, food, shelter, or medical) from text data, state-of-the-art Need classification techniques such as [16] focus on the identification of information type. However, these techniques do not provide a comprehensive solution for matching classification output. [5] merely summarizes disaster information at a certain geographical level without providing any actionable decisions for response and relief. Instead, they provide summaries of topics to decision makers. Our previous classification technique [3] uses an ontology and location detection to enhance situational awareness through recognizing the interdependencies between disaster events, which helps in the early identification of Needs. Inspired by these techniques, D-record employs a coarser Need classifier to make its classifications compatible with the affordances of locations (e.g., a shelter can support a variety of Needs such as food and water whereas a hospital can provide medical and rescue support *Needs*). In other words, we resort to coarser needs (as opposed to fine-grained needs) for effective matching because available help centers satisfy multiple needs.


Document: D record Disaster Response and Relief Coordination Pipeline
Section: 2 RELATED WORK
Type: text

. Our previous classification technique [ 3 ] uses an ontology and location detection to enhance situational awareness through recognizing the interdependencies between disaster events , which helps in the early identification of Needs . Inspired by these techniques , D - record employs a coarser Need classifier to make its classifications compatible with the affordances of locations ( e . g . , a shelter can support a variety of Needs such as food and water whereas a hospital can provide medical and rescue support * Needs * ) . In other words , we resort to coarser needs ( as opposed to fine - grained needs ) for effective matching because available help centers satisfy multiple needs .

**Matching.** Our previous technique on Seekers and Suppliers matching [18] uses hand-crafted rules to match the *Need* requested with a possible provider, e.g., matching someone offering shelter with someone requesting shelter. Other approaches to *Need* matching such as [6] uses structured input from users and utilizes moderators in the loop to match those *Needs* with organizations presently available to help. The *Need*-capacity matching technique by [17] quantifies the degree of impact of a disaster and prioritizes the matching accordingly. Finally, [14] provides both online and offline solutions for requesting, providing and coordinating resources.

Limitations of the above work include: (a) the inadequate assumption that all routes are available for matching by assuming the full accessibility of roads during a disaster [19], and (b) the matching problem being solved for a different level of granularity than for the required individual level of *Needs*.

**Flood Mapping.** Some logistical matching methods assume the full accessibility of routes. Hu et al. [7], for example, provides an algorithm for optimal path selection in a logistics supply-chain management system. In contrast, [15] provides a portal for UN staff to upload spatial data including satellite images and crowdsourced data that can identify blocked routes. We use our customized state-of-the-art human-guided flood mapping technique [11] to prune the flooded routes to reflect the situation on the ground relevant for better-informed transportation.

**Data Visualization.** The majority of the visualization techniques in the literature place geo-tagged tweets on a map [12] or display users' structured input such as in [20]. However, these techniques fail for social media-based systems where geo-tagged tweets are relatively infrequent, and the information is buried inside unstructured texts. [10] showed the effectiveness of layered visualization systems, where each layer can contain a different source of information. In D-Record, each layer can include a different *Need* class or OpenStreetMap location features. To visualize the kinds of


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Section: 2 RELATED WORK
Type: figure
Figure

Image /page/2/Figure/8 description: A diagram illustrating a system for disaster relief coordination and response, which processes data from multiple sources to create a layered map for matching seekers and providers. The process is shown in three main parts. The first part details the data sources on the left: 'Classify and Geoparse Tweets' using Twitter and LNEx; 'Satellite Image for Flood Mapping' using satellite imagery to identify flooded locations; and 'OpenStreetMap and Crowd-sourced Location Features'. The outputs from these sources are processed to 'Prune Flooded Locations' and are then combined into a stack of map layers shown in the center. The layers, from top to bottom, are: Flood Areas, Twitter Medical/Rescue Needs, CS Medical/Rescue Available, OSM Medical/Rescue Available, Twitter Shelter/Food/Supplies Needs, CS Shelter/Food/Supplies Available, OSM Shelter/Food/Supplies Available, and a base layer from MapBox. Each layer is associated with a specific icon. The final part, at the bottom, is labeled 'Disaster Relief Coordination and Response' and contains a box for 'Map Directions & Seekers/Providers Matching', illustrated with icons of a person, a map, and another person, signifying the matching of those in need with those providing aid.


Document: D record Disaster Response and Relief Coordination Pipeline
Section: 2 RELATED WORK
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Figure: Figure 1: D-record map layers and pipeline functions

Figure 1: D-record map layers and pipeline functions


Document: D record Disaster Response and Relief Coordination Pipeline
Section: 2 RELATED WORK
Type: text

*Needs* effectively they are bound to their spatial footprints extracted using our tool LNEx [2].


Document: D record Disaster Response and Relief Coordination Pipeline
Section: 3 METHOD
Type: text

The following section describes the different data sources, the functionality of the pipeline, and the map layers we employ that support the visualization of information. Figure 1 contains a summary of map layers and their purpose.

**Data Sources.** D-Record utilizes three data sources: Twitter, OpenStreetMap, and Satellite Images. Twitter data consists of two groups. The first group contains a set of two targeted streams from two disaster events, namely, the Chennai Flood in 2015 and Houston Flood in 2016 [2]. The second group of crisis tweets is from CrisisNLP [9] and CrisisLexT26[16] datasets labeled for their information type (e.g., affected individuals, donations, and volunteering). The first group drives the pipeline. The second is used to train a binary classifier to recognize the expression of *Need* in tweets and expand the concepts in the underlying ontology (see Section 3). Crowd-sourced data from volunteers (contributed as Excel sheets) contain information about shelters and help requests/offers (e.g., Chennai Flood - List of Corporation and Relief Centres5).

The OpenStreetMap gazetteer provides location names and metadata (such as geo-coordinates) that produce the affordances of each location with respect to the needed help (e.g., a hospital can provide rescue and medical support) allowing for matching a help offer with a *Need* request from tweets. The Output of our flood mapping tool [11], which uses satellite images, determines the flooded geo-points and overlays this information on top of the map.

**Location-Centric Ontological Modeling.** Determining spatially and temporally specific instances for coordinating resources *Needs* benefit from a domain-specific location-centric event ontology for social media data. Event location is key to both aggregation and analysis of related event instances. Therefore, we created an event ontology that uses information from past disaster and risk reports6 to enrich existing disaster event vocabularies.

The D-Record ontology7 represents the temporal distinction between the response and relief phases of a disaster. The class "Concepts" includes two kinds of concepts: *Needs* and Availability. The class OSMFeatures covers the affordances of various location types (i.e., OSM map features8) in relation to the *Need* classes.

<sup>5https://rebrand.ly/reliefspreadsheet

6 such as by https://www.acaps.org/

<sup>7The ontology can be found at https://github.com/shrutikar/d-record.

<sup>8http://wiki.openstreetmap.org/wiki/Map\_Features


Document: D record Disaster Response and Relief Coordination Pipeline
Section: 3 METHOD
Type: figure
Figure

Image /page/3/Figure/2 description: A flowchart detailing a data processing pipeline for disaster response and scenario dissemination. The process begins on the left with a 'Data' section, which includes three sources: 'Stream and Crowd Sourced Data' (with icons for Twitter and Excel sheets), 'Pre-Disaster Data' (with the OpenStreetMap logo), and 'Satellite Imagery' (with the NOAA logo). The data flows through a series of processing steps. Stream and crowd-sourced data undergo 'Semantic Filtering' and then 'Information Retrieval/Extraction'. This extraction process, also fed by pre-disaster data, branches into 'Geoparsing' (using LNEx), 'Seekers / Providers need classification', and 'OSM location classification with map feature types'. The 'Seekers / Providers' data is further processed by 'Event Ontology & Situational Data' using 'Lexicon NLP'. Satellite imagery is used for 'Flood Mapping'. The processed data streams converge: 'Geoparsing' and 'Event Ontology' data become 'Typed geolocated data', while 'OSM location classification' and 'Flood Mapping' data are used to 'Prune/Filter Locations'. Both of these outputs are then stored via 'Caching in ElasticSearch'. From the cache, 'map layers' are created using 'mapbox'. These layers are used for 'Seekers / Providers Matching'. This matching process leads to 'Provide turn-by-turn directions with contact information', which is also informed by 'Prune non-available routes' from the flood mapping. There is a feedback loop from providing directions back to the matching process. The final step is 'Scenario Dissemination', illustrated by a map with colored risk zones and a route between points X and A.


Document: D record Disaster Response and Relief Coordination Pipeline
Section: 3 METHOD
Type: figure
Figure: Figure 2: D-record system architecture

Figure 2: D-record system architecture


Document: D record Disaster Response and Relief Coordination Pipeline
Section: 3 METHOD
Type: table
Table

| Need Class              | CrisisNLP and CrisisLexT26 Classes                                                     |
|---------------------------------------|-----------------------------------------------------------------------------------------------|
| Shelter/Food/ Supplies Need | donation_needs_or_offers_or_volunteering_services, displaced_people_and_evacuation         |
| Medical/Rescue Help Need    | missing_trapped_or_found_people, deaths_reports, in- jured_or_dead_people, affected_people |

Need Class
CrisisNLP and CrisisLexT26 Classes


Document: D record Disaster Response and Relief Coordination Pipeline
Section: 3 METHOD
Type: text

Table 1: Our mapping of CrisisNLP and CrisisLexT26 classes

The set of keywords for a "Needs" concept was expanded using topic modeling learned from the labeled data for the two classes: Shelter/Food/Supplies and Medical/Rescue Help. The relevance probability of each word to the Need class/topic functioned as a feature while training our Need classifier as shown in Section 3. Finally, the sub-classes of the Needs concept are mapped to the OSMFeatures sub-classes representing the options for available help at each location type (i.e., the affordances). For example, hospital maps to the Medical/Rescue Help class and pharmacy maps to the Shelter/Food/Supplies Need class.

**Text Classification.** To leverage the existing labeled data from CrisisNLP and CrisisLexT26, we combined the overlapping classes to form the two *Need* classes (see Table 1). We used tweets from these datasets to train an SVM-based classifier which allows Drecord to categorize any given crisis related text into one of the two classes: Shelter/Food/Supplies *Need* or Medical/Rescue Help *Need*. The *Need* class Shelter/Food/Supplies includes donations, volunteer services, and *Needs* for food, water, shelter, or clothes. The *Need* class Medical/Rescue Help comprises affected people, death reports, injured or dead people, missing or trapped people, and other medical and rescue-related information. The classified streaming text appears on the map for matching.

The classifier had to capture tweet semantics. A simple Bag-of-Words model lacks context. Instead, feature engineering [22] vectorizes the text sentences to capture their semantics adequately. Before featurizing, the text was preprocessed by stemming, case folding and removing "noisy" lexical elements (such as URLs, non-ASCII characters, mentions, punctuations, dataset-specific stopwords, and hashtags). Finally, we designed an SVM classifier with lexicon-based features, TF-IDF vectors, and gensim's word2vec embeddings9.

Using the relevance probabilities from topic modeling described earlier, we form lexicon-based two-tuple feature vectors (i.e., Shelter/Food/Supplies and Medical/Rescue Help). Each element in the vector represents the word frequency multiplied by its relevance score. This vector was concatenated with the other features from TF-IDF and gensim's word2vec embeddings, which captures the semantics of a word by looking at the context where it was mentioned. To address the class imbalance, SMOTE oversampled the minority class synthetically.

**System Architecture.** D-record uses three major forms of data (see Figure 2): Streaming and Crowdsourced, pre-disaster, and satellite imagery data. The knowledge extracted from text streams (filtered using hashtags of the disaster event) and crowdsourced excel sheets provides situational awareness and the various kinds of help available at each location. The pre-disaster information available from OpenStreetMap (sliced using a bounding box of the disaster event) represented the available help confirmed or pruned using satellite images. The output from our flood mapping method [11] helped prune out routes that were unavailable during the matching process for the location seeking help.


Document: D record Disaster Response and Relief Coordination Pipeline
Section: 3 METHOD
Type: text

* * D - record uses three major forms of data ( see Figure 2 ) : Streaming and Crowdsourced , pre - disaster , and satellite imagery data . The knowledge extracted from text streams ( filtered using hashtags of the disaster event ) and crowdsourced excel sheets provides situational awareness and the various kinds of help available at each location . The pre - disaster information available from OpenStreetMap ( sliced using a bounding box of the disaster event ) represented the available help confirmed or pruned using satellite images . The output from our flood mapping method [ 11 ] helped prune out routes that were unavailable during the matching process for the location seeking help .

Our tool LNEx extracts and attaches each location to their latlong information which locates tweet information on the map, following *Need* classification. Since OpenStreetMap provides the metadata for locations (including names, types, and geo-coordinates), the flood map information is used to prune out the locations that are flooded and not accessible for help. D-record caches all of this information in Elasticsearch. The *Need* information and location of a tweet indicate where the help is needed while OSM location features identify available help. We create the map layers on top of MapBox10. The system then matches a *Need* in the ontology with the locations that can fulfill that *Need*. D-record ultimately prunes all routes containing a flooded section according to the flood map.


Document: D record Disaster Response and Relief Coordination Pipeline
Section: 4 RESULTS
Type: text

SVM with the SMOTE (to partially overcome class imbalance) and the Gradient Boosting Algorithm performed the best and achieved a 0.8 F-Score in the *Need* classification task. We tested it using the leave-one-out technique (i.e., testing on one dataset and training using the rest). D-record extracted locations from tweets to support plotting these tweets on the map. The experiments used our Chennai and Houston datasets, containing 169,838 and 415,057 tweets, respectively. From these tweets, LNEx extracted 85,564 locations from the Chennai dataset and 241,684 locations from Houston dataset. As for the number of OpenStreetMap location features, 1,103 and 2,826 locations were retrieved for the affected areas of Chennai and Houston, respectively. Other crowdsource data received from the excel sheets were relatively few, around 41 locations.


Document: D record Disaster Response and Relief Coordination Pipeline
Section: 5 DEMONSTRATION
Type: figure
Figure: Figure 3 shows D-record where users can choose the time range to filter the data using ⓐ, the dataset using ⓑ, and select the map layers using ⓒ. To match a Need , the user clicks on the orange icons as in ① which shows a textbox with the tweet text and the extracted location. Users can click on a "Match Need" button to get the closest and non-flooded nearby location which can provide help for the given Need , as in ②. Users obtain the matched location information by hovering the mouse over the green icons as in ③. The tool will provide the full information of the matched location and the contact number if available with the turn-by-turn directions obtained using MapBox directions API11 as shown in ④.

Figure 3 shows D-record where users can choose the time range to filter the data using ⓐ, the dataset using ⓑ, and select the map layers using ⓒ. To match a *Need*, the user clicks on the orange icons as in ① which shows a textbox with the tweet text and the extracted location. Users can click on a "Match Need" button to get the closest and non-flooded nearby location which can provide help for the given *Need*, as in ②. Users obtain the matched location information by hovering the mouse over the green icons as in ③. The tool will provide the full information of the matched location and the contact number if available with the turn-by-turn directions obtained using MapBox directions API11 as shown in ④.


Document: D record Disaster Response and Relief Coordination Pipeline
Section: 5 DEMONSTRATION
Type: text

9https://radimrehurek.com/gensim/

10 https://www.mapbox.com/

11https://www.mapbox.com/help/define-directions-api/


Document: D record Disaster Response and Relief Coordination Pipeline
Section: 5 DEMONSTRATION
Type: figure
Figure

Image /page/4/Figure/2 description: A screenshot of a disaster management user interface called "HazardsSEES". The interface is split into a left sidebar and a main map view. The left sidebar has a logo, a time range selector set to "12-01-2015 1:30 PM - 01-30-2016 2:00 PM", a location selector with "Chennai" selected, and a legend for map icons such as "Flooded Areas", "Medical/Rescue Help Available", and "Shelter/Food/Supplies Needed". The main map area displays a map of Chennai with purple patches indicating flooded areas. On the map, there is a pop-up window showing a message: "@Uber\_Chennai - Need help in rescuing a group of girls from Perumalagaram Salai Chennai! #ChennaiMicro #ChennaiFloods" with a "Match Need" button. A blue line indicates a route on the map. Another pop-up identifies the "ACS Medical College And Hospital". In the bottom right, a box shows "Matched Location Info" for the hospital, including its address and turn-by-turn directions.


Document: D record Disaster Response and Relief Coordination Pipeline
Section: 5 DEMONSTRATION
Type: figure
Figure: Figure 3: Example screenshot of the D-record tool.

Figure 3: Example screenshot of the D-record tool.


Document: D record Disaster Response and Relief Coordination Pipeline
Section: 6 CONCLUSIONS AND FUTURE WORK
Type: text

A domain-specific location-centric event ontology is crucial for situation awareness and disaster response. We demonstrated our D-record pipeline for *Need*-offer matching and discussed the functionality of the system and the multi-modal data used to fire its engine. The pipeline can be used to match coarse-grained *Needs* with possible suppliers meaningfully using location information available on the map. In the future, a finer-grained classifier can be designed to do more flexible or specific matching. Additionally, we plan to develop a custom entity extractor building on our prior work [1], to extract emerging entities during the onset of a disaster for more advanced spatiotemporal reasoning. We also plan to use weather data and background knowledge to mark flood-prone areas to help in preparedness in addition to response. To empower first and local responders, we intend to bring this pipeline for broader use by the disaster response community and port it to smartphones.


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

Glacial Lake Outburst Flood (GLOF) has become a crucial aspect as the increase in the meltdown of glaciers results in the breach of unstable debris dams. Hence, it is essential to understand the nature of the glacial lakes for proper planning and development of the region in the long term. In this paper, a deep learning network is developed for GLOF hazard and risk assessment. The Shepard Convolutional Neural Network Fused Deep Maxout Network (ShCNNFDMN) is developed by fusing the Shepard Convolutional Neural Networks (ShCNN) and the Deep Maxout Network (DMN) based on regression analysis. Here, various data and feature attributes, like geometric properties, location properties, lake-based properties, and global properties are determined from the glacial lake data. Afterthat, hazard assessment is carried out based on these parameters by the ShCNNFDMN. Then, risk assessment is performed based on the hazard levels and the feature attributes. The ShCNNFDMN is analyzed based on metrics, such as Hazard modelling error, Risk prediction error, Mean Average Error (MAE), and R-Squared are found to produce values of 0.462, 0.423, 0.358, and 0.288, respectively. The proposed method is useful in applications, like infrastructure planning, taking preventive and mitigative actions in downstream areas of glacier lakes.

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: ABSTRACT > ARTICLE HISTORY
Type: text

Received 30 October 2023 Accepted 10 June 2024


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

GLOF; risk assessment; hazard assessment; Shepard Convolutional Neural Networks; Deep Maxout Network


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 1. Introduction
Type: text

Global warming and increased human activities have caused major changes in the drylands, and terrestrial water resources all over the world (Zhang et al. 2023). The high voluminous melting glacier water runoff, and accumulates in front of receding glaciers or depressions over sagging and thinning glacier surfaces creating moraine-dammed lakes (Sattar et al. 2021). Generally, glacier shrinking results in the expansion and creation of a huge number of glacial lakes on/in front of the glaciers (Wang et al. 2018). A huge volume of water is released because of certain failures in the moraine or sudden emptying of these lakes due to the overflow of dams, where the destructive events caused by released water sediment are termed Glacial Lake Outburst Floods (GLOF) (Liu et al. 2020; Worni, Huggel, and Stoffel 2013). The GLOF is characterised by its potential for erosion and high transport (Breien et al. 2008) and it can be effectively converted into debris

flows of about 1.5 tm<sup>-3</sup> densities. It is also due to dynamic slope movements of the glaciers over the lakes, namely slopes (Wang and Goh 2021), landslides, rock falls, or icefalls (Awal et al. 2010; Emmer and Vilímek 2013). The incidence of GLOF has been high, especially in the Himalayan area, and has posed a tremendous risk to the local inhabitants (Li et al. 2018). Further, the quantity of water flow in the river affects various activities, such as the allocation of water for hydropower projects, irrigation systems, ecosystems, etc. (Ali et al. 2020).

An in-targeted framework based on the adaption of climate change as well as glacier hazard management is performed from the lessons of the devastating GLOF events (Wang et al. 2018). Over the past decades, the GLOF hazard has increased the risk level due to the significant growth of the existing glacial lakes (Sattar, Goswami, and Kulkarni 2019). The impact of GLOF is transboundary, where the floods destroy most of the downstream infrastructure and kill hundreds of people

thus causing huge damage (Khanal et al. 2015; Liu et al. 2020). Based on a regional scale, the GLOF hazard evaluation is often divided into three parts. The first stage involves gathering the fundamental data about glacial lakes, including their size, position, and surrounding terrain. In the second stage, glacial lakes that are potentially dangerous are identified using the variables that have been chosen. Finally, by using model simulation, field investigation, and high-resolution remote sensing images the study of dangerous glacial lakes is performed (Huggel et al. 2004; McKillop and Clague 2007; Osti, Egashira, and Adikari 2013; Wang et al. 2018). The implementation of different GLOF-related hazard remediations is carried out to develop a significant hazard management system (Sattar, Goswami, and Kulkarni 2019). The GLOF hazard assessment models are developed by considering the glacial lake outburst debris flow scenario (Worni et al. 2012) after earthquakes and small glacial lakes in the areas, which are familiar for landslides and collapses along the channels (Liu et al. 2020). Numerous approaches have been proposed in the past for performing GLOF risk assessment and these techniques vary from each other depending on the subjectivity, input data, choice of examined characteristics, quantity, structure, and so on (Washakh et al. 2019).


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 1. Introduction
Type: text

remediations is carried out to develop a significant hazard management system ( Sattar , Goswami , and Kulkarni 2019 ) . The GLOF hazard assessment models are developed by considering the glacial lake outburst debris flow scenario ( Worni et al . 2012 ) after earthquakes and small glacial lakes in the areas , which are familiar for landslides and collapses along the channels ( Liu et al . 2020 ) . Numerous approaches have been proposed in the past for performing GLOF risk assessment and these techniques vary from each other depending on the subjectivity , input data , choice of examined characteristics , quantity , structure , and so on ( Washakh et al . 2019 ) .

In general, because of the potentially hazardous conditions in high mountains, the assessment of glacier hazards cannot be based solely on historical records and past events. Hence, it is necessary to implement various modelling approaches for the determination of present and future GLOF risks and hazards (Allen et al. 2016; Frey et al. 2018; Schaub et al. 2013; Schneider et al. 2014). Generally speaking, identifying exposure, vulnerability, and convergence of hazards are the fundamental elements utilised in risk assessment and management. The subset of machine learning techniques based on representation learning and artificial neural networks is known as deep learning (Wang et al. 2023). Images are processed using a convolutional neural network (Wu et al. 2023). for object detection and classification. The relative GLOF risk towards the downstream region is caused by the cumulative and peak discharge of the hazard components. This leads to overtopping, potential GLOF triggers, damming moraine conditions, and drainable volume functions of the lake. It is also helpful for the determination of hazards based on the vulnerability and exposure of elements downstream of the lake (Emmer 2018; Frey et al. 2018). Moreover, for responsive disaster preparedness and mitigation implementing detailed models and assessments are essential to address extreme-magnitude scenarios (Sattar et al. 2021).


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 1. Introduction > 1.1. Motivation
Type: text

In the Himalayan region, glacial lakes have emerged and expanded quickly as a result of glacier recession brought on by climate change. The region is now more vulnerable to Glacial Lake Outburst Floods (GLOFs) as a result of the increased melting. The infrastructure and way of life in the nearby low-lying communities could suffer if potentially hazardous glacier lakes collapsed catastrophically. GLOF has become a crucial aspect in the economic and social stability of the downstream areas as the increase in the meltdown of glaciers results in the breach of unstable debris dams. Hence, it is essential to understand the nature of the glacial lakes for proper planning and development of the region in the long term. Also, the prevailing works carried out with a focus on GLOF risk assessment with their merits and problems confronted that motivated the development of the ShCNNFDMN.

The major intention of this research is to implement a GLOF risk assessment model using the proposed ShCNNFDMN. Here, the GLOF risk assessment is carried out by considering the various properties of the glacial lake. Initially, properties, like geometric, location, lake-based, and global properties are excerpted from the input data. Thereafter, these features are applied to the ShCNNFDMN for assessing the hazard and risk. Here, the ShCNNFDMN is modelled by fusing the ShCNN with the DMN based on regression modelling. At first, the hazard assessment is carried out by the ShCNNFDMN based on the extracted features, and thereafter, the ShCNNFDMN carries out the risk assessment using the features as well as the hazard levels.

The key contribution of this work is as follows:

• Proposed ShCNNFDMN for risk/hazard assessment: In this work, GLOF risk and hazard assessment is carried out by using the ShCNNFDMN, which is formulated by combining ShCNN with DMN, based on regression modelling to improve the efficiency of assessment. Here, fusion is accomplished by applying the concept of Fractional Calculus (FC) to produce a regression model.

The remaining part of the work is arranged as given: section 2 depicts the related works, section 3 portrays the ShCNNFDMN proposed in this work for GLOF risk assessment. The experimental results are shown in Section 4, and Section 5 concludes the study with recommendations for enhancements.


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 2. Literature review
Type: text

Sattar et al. (2021) proposed a physical hydrodynamic model for modelling the lake outburst and hazard

assessment of the glacial lakes. This method was developed for assessing the GLOF risks across the Barun-Arun river valley in Nepal. Here, the hydraulic flow of the river at six downriver encampments was analyzed, and the possible impact at every location was estimated by evaluating two low-level, two moderate-level, two high-level, and two extreme-level magnitudes were investigated for the current lake size and using this, the future dimension of the lake was modelled. Liu et al. (2020) developed a GLOF hazard assessment model for analyzing the risk of GLOF in Bhote Koshi Basin (BKB). This model assessed the GLOF hazard levels by regarding the situation that the numerous landslides caused by earthquakes evolved into debris in the outburst flood thereby enlarging the volume and discharge of the debris flow. This approach was effective in comprehending the data required for GLOF hazard analysis. However, the method was not appropriate for very high-hazard glacial lakes. Saifullah et al. (2020) devised a Remote sensing technique for GLOF risk assessment along the China-Pakistan Economic Corridor (CPEC). This research was aimed at investigating the formation and impacts of barrier lakes, supra-glacial, and end moraine based onsitu and remote sensing approaches in the Hunza River basin along CPEC considering the peak discharges and volume of the lakes. Emmer and Vilímek (2013) studied the Lake and breach hazard assessment model for assessing the risks of GLOF of moraine-dammed lakes. In this work, various methods for assessing the lake and breach hazards were outlined and the approaches were applied for assessing the hazard levels of the moraine-dammed lakes in Cordillera Blanca (Peru).

Wang et al. (2018) introduced a Hydrologic Engineering Center-River Analysis System (HEC-RAS) for carrying out the integrated hazard assessment of the Cirenmaco glacial lake. This method combined various approaches, like 2D hydraulic modelling, bathymetric survey, and remote sensing for assessing the hazards presented by the Cirenmaco glacial lake. The results produced by the approach were shown to be highly effective in mitigating the risks, but specific care was needed when examining the outcomes of the model because of the complicated nature of GLOFs. Sattar, Goswami, and Kulkarni (2019) developed a two-dimensional hydrodynamic modelling approach for assessing the GLOF hazards of South LhonakLake. Here, a realistic bathymetric framework was constructed for depicting the various GLOF scenarios, and the hazard probability was assessed by utilising one and two-dimensional hydrodynamic modelling techniques. This method made it easier to build structures in the middle of the flow channel and may help manage the risk that extreme flow occurrences bring to areas downstream. Unfortunately, the approach did not provide in-


Document: Deep learning-based GLOF modelling for hazard assessment and risk management
Section: 2. Literature review
Type: text

the risks , but specific care was needed when examining the outcomes of the model because of the complicated nature of GLOFs . Sattar , Goswami , and Kulkarni ( 2019 ) developed a two - dimensional hydrodynamic modelling approach for assessing the GLOF hazards of South LhonakLake . Here , a realistic bathymetric framework was constructed for depicting the various GLOF scenarios , and the hazard probability was assessed by utilising one and two - dimensional hydrodynamic modelling techniques . This method made it easier to build structures in the middle of the flow channel and may help manage the risk that extreme flow occurrences bring to areas downstream . Unfortunately , the approach did not provide in -

situ measurements of the geomorphic characteristics over the flow channel or an analysis of engineering properties. Frey et al. (2018) proposed a Scenario-based multisource GLOF hazard mapping technique for assessing the risks of multisource GLOFs. Here, the cascaded mass movement of debris was simulated by utilising a chain of interrelating numerical models. Later, vulnerability evaluations for breach formation and rock-ice avalanches were employed for defining the events of related probability and varying magnitude. However, it failed to enhance the hazard mapping in urban areas that includes a detailed computation of the impacts of urban infrastructure on the flow behaviour of GLOFs. Li et al. (2018) used an Unmanned Surface Vessel (USV) and remote sensing to assess the hazards of GLOF in the Jialong Co glacial lake. Geomorphological analysis, volume assessment, and area dynamics were used for estimating the hazard potential of Jialong Co. The evolution of the Jialong Co in the last years was analyzed using Sentinel 2 satellite and Landsat series image data. Then, the volume, and underwater topography of the lake were detected utilising USVs for bathymetric measurements. Later, the hazard potential was determined by combining the geomorphic conditions, volume data, and area dynamics obtained from in-situ measurements. This plan assisted in determining the volume and underwater topography of the lake, but it did not improve accuracy or carry out longterm geographic monitoring of the moraine dam. Ahmed et al. (2022). uses a combination of remote sensing, GIS, and dam break modelling to evaluate the GLOF danger of Gangabal Lake, which is situated in the Upper Jhelum basin of the Kashmir Himalaya. Glab-Top-2, multitemporal satellite data, and the Cosi - Corr model were also used to evaluate the parameters of the Harmukh glacier, which feeds Gangabal Lake. Wangchuk and Tsubaki (2024) studied the possible effects of a GLOF coming from one of Bhutan's biggest and fastest-growing glacial lakes, Thorthomi, which is a glacial lake in the Phochhu River Basin. The findings here highlight the critical necessity to comprehend and get ready for the possible aftermath of a GLOF from Thorthomi Lake in order to lessen the effects on downstream ecosystems, economies, and societies.


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
Type: text

In the current era, the dramatic rise in temperature worldwide due to global warming has resulted in an increase in the number of glacier lakes as well as the dimension of the existing ones. These phenomena have led to a potential risk of GLOF that can wreak havoc on man-made infrastructure and the natural environment. Hence, it is necessary to assess the risk of GLOF to avoid and mitigate any undue casualties. This paper proposes a hybrid ShCNNFDMN-based GLOF risk assessment technique, which is implemented as follows. Gathering information from the dataset is the primary step in the risk assessment process. After that, a number of features, including geometric, locationbased, lake-based, and global properties, are ascertained by applying the input data to the data and feature attribute extraction phase. The location properties that are taken into consideration are indicated by the latitude, longitude, and altitude, with the geometric property being the shape of the lake. Additionally, global characteristics like the Global Terrestrial Network for Glaciers (GTN-G) region and the Universal Transverse Mercator (UTM) grid zone, as well as lake-based parameters like lake area, perimeter, water volume, type, and uncertainty, are also extracted from the input data. Following the mining of the data and feature properties, the suggested hybrid deep learning method ShCNNFDMN is used to assess the hazards. The ShCNN (Ren et al. 2015) and DMN (Sun, Su, and Wang 2018) are combined to create the suggested ShCNNFDMN. The GLOF hazard is evaluated in this instance and assigned to one of the following five categories: Very High (VH), High (H), Medium (M), Low (L), and Very Low (VL). Lastly, the suggested ShCNNFDMN is used to execute the risk assessment based on the data, feature properties, and hazard levels. The GLOF risk is also divided into five categories, which include VH, H, M, L, and VL. The suggested ShCNNFDMN's structural view for the GLOF risk assessment is displayed in Figure 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 > 3.1. Data acquisition
Type: text

The initial step in GLOF assessment is to aggregate the data from the GLOF inventory dataset (https://zenodo. org/record/4477945#.ZGXf7nZBxPZ), which can be formulated as.

$$C = \{C_1, C_2, \dots, C_i, \dots, C_c\}$$
 (1)

where,  $C_i$  refers to the *i*th record contained in the dataset  $C_i$ which is considered for GLOF risk prediction, and c specifies the total number of records contained in the dataset.


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
Type: text

Once the data  $C_i$  is retrieved, it is subjected to the extraction of various feature attributes, such as geometric properties, location properties, lake-based properties, and global

properties are determined from the input data. The main intention of this task is to identify the key features of the lake, thereby enabling efficient hazard and 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 > 3.2.1. Geometric properties
Type: text

The geometric property of the glacial lake is considered an important aspect as it determines the stability of the lake. Here, the shape of the lake is taken into account and is found from the input lake image. At first, the boundary values of the lake are computed and then the image is split up into multiple grids. The grids that are contained inside the boundary of the lake are assigned a value of "1", and those falling outside the boundary are allocated a value of "0". Thus, a shape feature matrix comprising of the binary value is obtained from the input lake image, and the shape index matrix thus generated is represented as N.