Multi-dimensional assessment of flood susceptibility drivers in the urban watershed of Guwahati - Ahmed, Ishita Afreen; Talukdar, Swapan; Naikoo, Mohd Waseem; Shahfahad; Rahman, Atiqur (Acta geophysica, 2025)
Floods are a critical natural disaster causing widespread global devastation, exacerbated by the interaction of natural hydrological phenomena and human activities, particularly in rapidly urbanizing areas like Guwahati, India. The present study systematically examines the influence of sixteen environmental and socioeconomic parameters on flood dynamics using a comprehensive modeling framework that combines statistical analysis, advanced machine learning (ML) algorithms, and four feature selection methods: genetic algorithm (GA), Boruta, recursive feature elimination (RFE), and recursive partitioning (Rpart). The application of ML models—support vector machine (SVM) and random forest (RF)—with GA-derived parameters resulted in high predictive performance (accuracy: 0.892; precision: 0.887; recall: 0.868), while deep neural networks (DNNs) integrated with explainable artificial intelligence (XAI) enabled robust sensitivity analysis. The results show a strong positive correlation between the topographic wetness index (TWI) and land use and land cover (LULC) with flooding events. Conversely, factors such as proximity to agricultural land and built-up areas show a negative correlation. LULC, precipitation, distance to built-up areas, and population density were identified as important factors influencing inundation patterns. Furthermore, rainfall, LULC, and proximity to built-up areas emerged as key predictors, with SHAP values highlighting rainfall as the most influential driver of flood risk. Spatial analysis delineated approximately 773 km 2 as very low and 38.47 km 2 as very high susceptibility zones. These findings of the study offer actionable insights for urban planners, policymakers, and disaster management authorities to design targeted mitigation strategies, inform zoning regulations, and prioritize resilient infrastructure development. The approach also contributes to broader urban climate resilience strategies, aligning with SDG 11 and SDG 13 objectives.