Optimal flood susceptibility model based on performance comparisons of LR, EGB, and RF algorithms

Résumé

Abstract The Wadi El-Matulla, located in the eastern desert of Egypt, is the most important water basin. The Qift - Qusayr highway (west-east direction) and the Cairo - Aswan eastern desert highway (north-south) pass through the basin. Many urban areas (villages and industrial areas) and agricultural lands are located at the outlet of these basins. In addition, the basin has a promising potential for future economic and urban development as it is located within the Golden Triangle (governmental megaproject). The current study investigates flood hazard modeling and its impact on the area. To determine the optimal algorithm for flood susceptibility mapping, performance comparisons of three techniques were conducted: logistic regression (LR), extreme gradient boosting (EGB), and random forest (RF). Remote sensing, topographic, geologic, and meteorological data were used with the help of field visits to provide spatial database and inventory datasets required by the models. The models’ performances were assessed using five statistical indices, ROC, overall accuracy (OAC), kappa index (Ka), RMSE, and mean absolute error (MAE). Our findings indicate that RF is the optimal algorithm for flood susceptibility mapping, followed by EGB and LR. RF provides high performance with high values of ROC (93%), OAC (88%), and Kappa index (0.85) and the lowest values of RMSE (0.34) and MAE (0.12). Finally, the RF model was verified using sentinel-1 images for real floods in 2016 and 2021, and it provides good agreement. The optimal model could help the decision makers and planners to protect the existing facilities and plan the future projects in non-flood prone areas.

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