Integrating energy valley optimization with machine learning for flood susceptibility mapping in Kayseri, Türkiye

Abstract This study responds to the growing concern about flooding and its consequences, particularly in areas prone to severe meteorological events, by employing an innovative approach to identify flood susceptibility in Kayseri. The methodology combines machine learning (ML) algorithms, namely extreme gradient boosting (XGB), categorical boosting (CB), and gradient boosting (GB), with hyperparameter optimization strategies through a hybridization process utilizing the energy valley optimizer technique. A total of 6000 data points were designated for the purposes of training, testing, and validation. In order to create these models, a total of nine variables, which have been identified as influential factors in the occurrence of floods, were selected based on data availability and a review of the relevant literature. It is noteworthy that elevation and rainfall were identified as pivotal predictors across all models. The CB model demonstrated robust predictive accuracy, with a substantial majority of instances correctly classified. The AUC values for the XGB and GB models remain notably high at 0.98, indicating robust predictive power and generalization capabilities. In the test phase, the AUC values underscore the superior performance of the XGB (0.9763) and GB (0.9739) models, with the CB model also demonstrating robust results at 0.9677. This study introduces a novel approach to flood susceptibility mapping by utilizing a range of ML methods. Its key innovations lie in the superior performance of these algorithms compared to traditional methods, as well as their inherent flexibility and heuristic capabilities. The generated flood susceptibility maps offer a detailed insight into the spatial distribution of flood susceptibility, with significant implications for urban planning and disaster preparedness.