Model Deployment

app.py

@@ -0,0 +1,115 @@
+import torch
+import gradio as gr
+import pickle
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+import pandas as pd
+from scipy.stats import norm
+from src.model import EnhancedLSTM
+
+def simulate_risk_score(rainfall, evaporation, inflow, outflow, population, water_usage_model, district):
+    """
+    Simulate and calculate the risk score for flood and drought, along with storage change details.
+
+    Parameters:
+    rainfall (float): Monthly rainfall in mm.
+    evaporation (float): Monthly potential evapotranspiration (PET) in mm.
+    inflow (float): Monthly reservoir inflow in m³.
+    outflow (float): Monthly reservoir outflow in m³.
+    population (int): Population of the region.
+    water_usage_model (callable): Predictive model for water usage based on population.
+    
+    Returns:
+    dict: Risk scores for flood and drought, including changes in inflow, outflow, and storage.
+    """
+    water_balance = rainfall - evaporation
+    #water_usage = 2e6
+
+    df = pd.read_csv('data/usage.csv')['District']
+    unique_districts = df.unique()
+    df = pd.DataFrame(unique_districts, columns=['District'])
+    one_hot = pd.get_dummies(df['District'], prefix='District')
+    df = pd.concat([df, one_hot], axis=1)
+    input = np.array(df[df["District"] == district].iloc[:, 1:].values.astype(int))
+    input = np.append(input, [[population]], axis=1)
+    input = torch.tensor(input, dtype=torch.float32).unsqueeze(0)
+    print(input.size())
+    
+    with torch.no_grad():
+        water_usage = water_usage_model(input).sum().item() * 1e6
+    
+    adjusted_inflow = inflow * (1 + water_balance / 100)  # Modify inflow based on water balance (rainfall/evaporation)
+    adjusted_outflow = outflow * (1 - water_balance / 100)  # Modify outflow based on water balance
+
+    # Calculate net water balance
+    net_water_balance = water_balance + (adjusted_inflow - adjusted_outflow) / 1e6
+    
+    # Calculate storage change in the district
+    storage_change = adjusted_inflow - adjusted_outflow  # Change in storage is the difference between inflow and outflow
+    
+    # Aggregate water balance for SPEI calculation
+    aggregated_balance = [water_balance, net_water_balance]
+    mu, sigma = np.mean(aggregated_balance), np.std(aggregated_balance)
+    print(mu,sigma)
+    spei = (net_water_balance  - mu) / sigma
+    
+    # Define flood and drought risks based on SPEI value
+    if spei <= -2:
+        drought_risk = "High Risk"
+        flood_risk = "Low Risk"
+    elif spei >= 2:
+        drought_risk = "Low Risk"
+        flood_risk = "High Risk"
+    else:
+        drought_risk = "Moderate Risk"
+        flood_risk = "Moderate Risk"
+    
+    
+    drought_score = max(0, min(100, (1 - norm.cdf(spei)) * 100)) * (water_usage / 1e6)
+    flood_score = max(0, min(100, norm.cdf(spei) * 100)) * (water_usage / 1e6)
+    
+    return {
+        "SPEI": spei,
+        "Drought Risk": drought_risk,
+        "Flood Risk": flood_risk,
+        "Drought Score": drought_score,
+        "Flood Score": flood_score,
+        "Adjusted Inflow": adjusted_inflow,
+        "Adjusted Outflow": adjusted_outflow,
+        "Storage Change": storage_change,
+    }
+
+input_size = 14
+output_size = 3
+model = EnhancedLSTM(input_size=input_size, lstm_layer_sizes=[128]*3, linear_layer_size=[64]*6, output_size=output_size)
+model.load_state_dict(torch.load('model/res_1.pt', map_location='cpu', weights_only=False))
+model.eval()
+
+
+def predict(rainfall, evaporation, inflow, outflow, population, district):
+    result = simulate_risk_score(rainfall, evaporation, inflow, outflow, population, district)
+    return result
+
+# Gradio input-output setup
+inputs = [
+    gr.Number(label="Rainfall (mm)"),
+    gr.Number(label="Evaporation (mm)"),
+    gr.Number(label="Inflow (m³)"),
+    gr.Number(label="Outflow (m³)"),
+    gr.Number(label="Population"),
+    gr.Textbox(label="District")
+]
+outputs = gr.JSON(label="Risk Assessment Results")
+
+# Create the Gradio interface
+gr_interface = gr.Interface(
+    fn=predict,
+    inputs=inputs,
+    outputs=outputs,
+    title="Flood and Drought Risk Assessment",
+    description="Simulate and assess flood and drought risks based on environmental and demographic factors."
+)
+
+# Run the interface locally for testing
+if __name__ == "__main__":
+    gr_interface.launch()

model/res_1.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0ab90d8cc484a1d53fbd3a934e3a3ad6ec78e5b7ca6bd9557137f840ba6c6af1
+size 693794

requirements.txt

@@ -0,0 +1,5 @@
+torch
+gradio
+pandas
+numpy
+scipy

src/model.py

@@ -0,0 +1,51 @@
+import torch
+import torch.nn as nn
+import math
+
+class EnhancedLSTM(nn.Module):
+    def _init_(self, input_size, lstm_layer_sizes, linear_layer_size, output_size):
+        super(EnhancedLSTM, self)._init_()
+        self.input_size = input_size
+
+        self.lstm_layers = nn.ModuleList()
+        for i in range(len(lstm_layer_sizes)):
+            if i == 0:
+                self.lstm_layers.append(nn.LSTM(input_size, lstm_layer_sizes[i], batch_first=True, bidirectional=True))
+            else:
+                self.lstm_layers.append(nn.LSTM(lstm_layer_sizes[i-1]*2, lstm_layer_sizes[i], batch_first=True, bidirectional=True))
+
+        self.dropout = nn.Dropout(0.3)
+        self.fc_layers = nn.ModuleList()
+        for i in range(len(linear_layer_size)):
+            if i == 0:
+                self.fc_layers.append(nn.Linear(lstm_layer_sizes[-1]*2, linear_layer_size[i]))
+            else:
+                self.fc_layers.append(nn.Linear(linear_layer_size[i-1], linear_layer_size[i]))
+
+        self.output_layer = nn.Linear(linear_layer_size[-1], output_size)
+        self.relu = nn.ReLU()
+
+        self.apply(self.initialize_weights)
+
+    def forward(self, x):
+        out = x
+        for lstm in self.lstm_layers:
+            out, (hn, cn) = lstm(out)
+        out = self.dropout(out[:, -1, :])
+
+        for fc in self.fc_layers:
+            out = self.relu(fc(out))
+
+        out = self.output_layer(out)
+        return out
+
+    def initialize_weights(self, layer):
+        if isinstance(layer, nn.Linear):
+            nn.init.xavier_uniform_(layer.weight)
+            nn.init.zeros_(layer.bias)
+        elif isinstance(layer, nn.LSTM):
+            for name, param in layer.named_parameters():
+                if 'weight' in name:
+                    nn.init.xavier_uniform_(param.data)
+                elif 'bias' in name:
+                    nn.init.zeros_(param.data)

usage.csv

@@ -0,0 +1,182 @@
+District,Year,Month,Rainfall,Inflow From Other States,Consumption,Irrigation,Industry,Domestic,Built-Up,Agricultural,Forest,WasteLands,Wetlands,Waterbodies
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