Random Forest and CNN-Based Hybrid Modelling Approach for Susceptibility of Flood

Abstract

Floods are one of the most destructive natural disasters, causing significant socioeconomic losses, environmental degradation, and infrastructure damage. Effective flood susceptibility assessment is essential for disaster preparedness, risk mitigation, and urban planning. Traditional hydrological models often struggle to capture the complex interplay between climatic, topographical, and hydrological factors influencing flood occurrences. In response, machine learning techniques have emerged as powerful alternatives due to their ability to analyze large datasets and recognize intricate patterns. This study proposes a hybrid modeling approach that combines the strengths of Random Forest (RF) and Convolutional Neural Networks (CNN) to improve flood susceptibility prediction. RF is utilized for feature selection, identifying the most influential variables that contribute to flood risk, while CNN leverages spatial dependencies in geospatial data to enhance predictive accuracy. The methodology is applied to Düsseldorf, Germany, a flood-prone urban area, using a dataset that includes topographical, hydrological, and meteorological factors such as elevation, slope, distance to river networks, soil moisture, and extreme precipitation events. The results indicate that the hybrid RF-CNN model outperforms standalone models in both classification accuracy and spatial consistency. RF effectively ranks critical flood-inducing factors, while CNN provides high-resolution flood-prone area delineation. Key findings reveal that proximity to river networks, topographic wetness index, and extreme precipitation frequency are the dominant contributors to flood susceptibility. The hybrid approach bridges the gap between interpretability and precision, offering a comprehensive tool for flood hazard assessment. By integrating data-driven insights with spatial analysis, this study provides a robust framework that can aid policymakers, urban planners, and disaster management authorities in developing targeted flood mitigation strategies. The findings highlight the potential of hybrid machine learning models in enhancing climate resilience and improving early warning systems for flood-prone regions.