Prediction of flood-affected areas based on geographic information system data using machine learning

Abstract

Flood disasters have become more frequent and severe due to climate variability, posing significant threats to human lives, agriculture, and infrastructure. Effective disaster management and mitigation require accurate identification of flood-prone areas. This study develops an intelligent flood prediction system by integrating machine learning algorithms with geographic information systems (GIS) data to enhance flood risk assessment. The proposed system utilizes two machine learning models including random forest (RF) and support vector machine (SVM) to predict flood-susceptible areas. The models are trained on historical flood data and GIS-derived features, including elevation, slope, topographic wetness index (TWI), aspect, and curvature. The dataset undergoes preprocessing, including normalization and feature selection, before being divided into training, validation, and test sets. The models are then trained and evaluated based on their predictive performance. Evaluation metrics, particularly the area under the curve (AUC), demonstrate that RF outperforms SVM in predicting flood-prone areas. RF achieves an accuracy of 82%, while SVM records a lower accuracy of 68%. The superior performance of RF is attributed to its ability to handle complex, nonlinear relationships in flood prediction. These results highlight the effectiveness of machine learning algorithms in flood susceptibility modeling and support the integration of data-driven techniques into flood and disaster risk reduction management strategies.