Flood Vulnerability Assessment in Kochi Using GIS-AHP-ANN Integrated Modelling Approach

Abstract

The southern part of India, Kochi, is more vulnerable to flooding due to changing weather patterns, low-lying terrain, and fast urban growth. In this research, the likelihood of a flood occurrence using GIS and a hybrid forecasting approach that combines AHP and ANN were evaluated. Elevation, slope, stream density, Land Use and Land Cover (LULC), Topographic Wetness Index (TWI), and rainfall were all included in the weighted overlay approach used to calculate the Flood Vulnerability Index (FVI). To understand the blended risk assessments, an artificial neural network was trained using the Levenberg-Marquardt approach, AHP-derived weights, and the same physical elements. The ANN findings are dependable since the model generated accurate predictions and had a low mean squared error (R = 0.989). The cities, including Kaloor, Fort Kochi, and Palarivattom, are situated in all low-lying regions with a large population and inadequate sanitary facilities, putting the residents at serious danger. Combining the two results in a risk map that is more accurate than either AHP or ANN alone. Future attempts to strengthen communities, lessen the threat of storms, and improve the environmental quality of coastal regions may benefit from this.