Flooding is a complex, unpredictable disaster that occurs frequently and can have devastating impacts. Over the past two decades, the advent of machine learning (ML) methods has led to a surge in studies focused on flood prediction, emphasizing high-performance algorithms and fast processing times. The present study aims to investigate the challenges of generalization in flood prediction models using machine learning techniques. A dataset of 18,810 samples was compiled from 40 river basins covering the period 1959–2020. Nine machine learning algorithms were applied to the analysis: Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, AdaBoost, Gradient Boosting, Extra Trees, and Gaussian Naive Bayes. Four distinct validation methods were employed to assess the performance of the models, and the results were thoroughly analyzed. The Gradient Boosting model demonstrated exceptional validation performance indicating its robustness across diverse datasets. High accuracy was also observed in the Decision Tree, Random Forest, Extra Trees, and AdaBoost models. However, for datasets with fewer than 200 samples, these four models experienced a decline in performance. Elevation was identified as the most important factor influencing flooding in 36 basins. NDVI was the dominant factor in 3 basins, while rainfall was the main driver in only 1 basin. The results highlight the contributions and shortcomings of machine learning methods in sustainable flood disaster management systems.
Yükseler et al. (Sat,) studied this question.