Urban Flood Prediction Models Using GIS and Machine Learning

Abstract

Cities flooding is a major hydrological risk in rapidly urbanizing regions, intensified by climate change, extreme rainfall, and inadequate drainage systems. Traditional hydrological models require extensive calibration and high-resolution data, which are often unavailable for many cities. To address this issue, this study proposes a flood prediction framework that integrates Geographic Information Systems (GIS) with Machine Learning (ML). Multi-source geospatial data such as digital elevation models (DEM), land use–land cover (LULC), soil properties, drainage density, rainfall intensity, and historical flood records are used to derive flood conditioning factors in a GIS environment. These factors are then applied as inputs for supervised ML algorithms including Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting. Model performance is evaluated using RMSE, MAE, R², and ROC–AUC metrics. Results indicate that hybrid GIS-ML models outperform traditional approaches by effectively capturing non-linear relationships between hydrometeorological and urban factors. The framework provides a reliable decision-support tool for urban planners and disaster management authorities to identify flood-prone areas, improve drainage planning, and enhance early warning systems.