Integrating Hydrodynamic and Deep Transfer Learning Models for Enhanced Flood Risk Assessment in Urban Areas

To mitigate the probability of floods that may harm or obliterate urban infrastructure, residences, and lives, conducting flood risk assessments is imperative. Conventional flood maps are insufficient on their own because of the dependence of flood threats on variables such as population density, economic activity, and critical infrastructure. To enhance flood planning and response, cities can utilize EO data to continually revise flood risk maps. To mitigate the effects on people and the economy, accurate flood predictions and risk evaluations are essential. To develop an optimal flood risk assessment model, NDVI was employed for preprocessing and SA and IG feature selection techniques were implemented. This method enhances classification accuracy and comprehensibility by extracting statistical and contextual information from text. The Att-TM approach significantly improves classification performance, achieving a high accuracy level with an AUC value of 0.973. The integration of deep learning with traditional feature extraction methods enhances the reliability of urban flood risk assessment. To enhance the effectiveness of flood risk prediction and mitigation measures, future research should explore practical applications and further enhancements.