Novel ensemble models and their optimization based flood susceptibility modelling in Indian Himalayan Foreland Basin

Abstract

AbstractThis study focuses on the mapping of flood susceptibility in a specific region characterized by a low-altitude-range, sub-tropical monsoonal climate, and a riverine floodplain environment within the Middle Ganga Plain. To achieve this, four novel artificial intelligence model ensembles were employed. The aim was to compare the performance of these models in a distinct, tectonically active topoclimatic fluvial floodplain setting and determine the significance of different causative variables. The information obtained from this analysis can be highly valuable for planning and policymaking related to flood management principles. The entire Ganga Foreland Basin, which includes the Middle Ganga Plain, experiences frequent floods of varying magnitudes, underscoring the importance of this modeling exercise. To conduct this study, a comprehensive flood inventory and twelve selected flood conditioning factors were utilized in the development, testing (using 30% of the data), and validation (using another 30% of the data) of the four novel artificial intelligence models: LR-EBF, LR-FR, MLP-EBF, and MLP-FR. These models have been explored less in existing literature. The results revealed that the LR-based ensembles (LR-FR, LR-EBF: SRLR−FR= 86.7%, PRLR−FR= 83.9%, SRLR−EBF= 87.2%, PRLR−EBF= 84.7%) outperformed the MLP-based ensembles (MLP-FR, MLP-EBF: SRMLP−FR= 85.8%, PRMLP−FR= 82.8%, SRMLP−EBF= 86.4%, PRMLP−EBF= 84.4%) in the selected topoclimatic setting of the present study. Additionally, the LR-based ensemble with EBF demonstrated superior performance compared to the MLP-based ensemble with EBF. One notable finding of this study is the variation in performance among the four ensembles when applied in different topoclimatic and altitudinal range environments. The study revealed that the performance differences between LR-based ensembles employing both FR and EBF statistical models were consistent across all environments, exhibiting similar accuracy (in terms of AUROC) with less than a 5% disparity in success and prediction rates. This suggests that these models are likely to perform similarly across various environments. However, the performance differences in MLP-based ensembles, particularly with FR, were more significant, reaching up to a 10% disparity. Therefore, it is recommended to apply these models in high altitudinal range terrains with different topoclimatic settings to those considered in the present study. Notably, even the MLP-based ensemble with EBF displayed relatively consistent performance in varying topoclimatic and altitudinal range environments.