Examining LightGBM and CatBoost models for wadi flash flood susceptibility prediction

This study presents two machine learning models, namely, the light gradient boosting (LightGBM) and categorical (CatBoost), for first time predicting flash flood susceptibility (FFS) in Wadi System (Hurghada, Egypt). A inventory map with 445 sites was produced randomly divided into groups training (70%) testing (30%). Fourteen controlling factors were selected evaluated their relative importance occurrence prediction. The performance of models assessed using various indexes comparison to common random forest (RF) method. results show areas under receiver operating characteristic curves (AUROC) above 97% all that LightGBM outperforms other terms classification metrics processing time. developed FFS maps demonstrate highly populated are most susceptible floods. present proves employed algorithms (LightGBM CatBoost) can be efficiently used mapping.