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

Abstract

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 direction) pass through the watershed. Many urban areas (villages and industrial areas) and agricultural lands are located at the outlet of these basins. In addition, the basin has 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 flood susceptibility mapping algorithm, 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 the spatial and inventory database required by the models. The performance and reliability of the predictions of the proposed models were evaluated using five statistical indices: receiver operating characteristic–area under the curve, overall accuracy (OAC), kappa index, root mean square error (RMSE), and mean absolute error (MAE). The performance of the models showed that the values of ROC (93, 86 and 80%), OAC (88, 82 and 76%), kappa index (0.85, 0.75 and 0.51), RMSE (0.34, 0.42 and 0.49) and MAE (0.12, 0.18 and 0.24) for RF, EGB, and LR, respectively. Based on AUC values, RF and EGB models provide excellent and very good prediction for flood susceptibility. Our results show that RF is the optimal algorithm for flood susceptibility mapping, followed by EGB and LR. Consequently, the predictive power of RF model is quite good and the flood susceptibility map was classified into five classes, namely very low (51.7%), low (23.7%), moderate (16.2%), high (7.1%), and very high (1.3%). Ultimately, the RF model was verified using sentinel-1 imagery for real floods in 2016 and 2021, and it provides good agreement. The optimal model could be useful for decision makers and planners to protect existing facilities and plan future projects in non-flood-prone areas. Accordingly, the most suitable areas for future development need to be distributed mainly in the low and very low flood hazard areas.