GIS-Integrated Hydrological Model with Deep Learning for Industrial Flood Risk and Contaminant Spread Prediction

Abstract

Industrial regions located near rivers, drainage channels, and low-lying urban areas are increasingly vulnerable to flood events that can trigger hazardous contaminant dispersion and severe environmental impacts. Accurate prediction of flood behavior and contaminant transport is essential for effective industrial risk management and emergency response planning. This study presents a GIS-integrated hydrological model combined with deep learning techniques for industrial flood risk and contaminant spread prediction under varying environmental conditions. The proposed framework integrates geographic information systems, hydrological simulation, remote sensing data, and deep learning-based predictive analytics to improve flood forecasting accuracy and contaminant transport assessment in industrial environments. Spatial datasets including land use, elevation, rainfall intensity, drainage networks, soil properties, industrial facility locations, and contaminant source characteristics were incorporated into the modeling system to evaluate flood inundation patterns and pollutant migration behavior. Deep learning algorithms were employed to identify nonlinear relationships between hydrological variables and flood-contaminant interaction dynamics, enabling enhanced prediction capability under complex rainfall and runoff conditions. Simulation and analytical results demonstrated significant improvements in flood extent prediction, contaminant dispersion forecasting, and risk classification accuracy compared to conventional hydrological modeling approaches. The integrated framework also enabled rapid identification of high-risk industrial zones and vulnerable environmental receptors through real-time spatial analysis and predictive mapping.