Flood Susceptibility Assessment in Urban Areas via Deep Neural Network Approach

Abstract

Floods, caused by intense rainfall or typhoons, overwhelming urban drainage systems, pose significant threats to urban areas, leading to substantial economic losses and endangering human lives. This study proposes a methodology for flood assessment in urban areas using a multiclass classification approach with a Deep Neural Network (DNN) optimized through hyperparameter tuning with genetic algorithms (GAs) leveraging remote sensing data of a flood dataset for the Ibadan metropolis, Nigeria and Metro Manila, Philippines. The results show that the optimized DNN model significantly improves flood risk assessment accuracy (Ibadan-0.98) compared to datasets containing only location and precipitation data (Manila-0.38). By incorporating soil data into the model, as well as reducing the number of classes, it is able to predict flood risks more accurately, providing insights for proactive flood mitigation strategies and urban planning.