Abstract

The mountainous watersheds are increasingly challenged with extreme erosions and devastating floods due to climate change and human interventions. Hazard mapping is essential for local policymaking for prevention, planning the mitigation actions, and also adaptation to extremes. This study proposes novel predictive models for susceptibility mapping for flood and erosion. Furthermore, this study elaborates on prioritizing the existing sub-basins in terms of erosion and flood susceptibility. A comparative analysis of generalized linear model (GLM), flexible discriminate analyses (FDA), multivariate adaptive regression spline (MARS), random forest (RF), and their ensemble is performed to ensure highest predictive performance. Furthermore, the priority of the sub-basins in terms of sensitivity to erosion and flood was determined based on the best model. The results showed that the GLM, FDA, MARS, RF, and ensemble models had an area under curve (AUC) 0.91, 0.92, 0.89, 0.93 and 0.94, respectively, in modeling the flood susceptibility. Also, the GLM, FDA, MARS, RF, and ensemble models had an AUC of 0.93, 0.92, 0.89, 0.96, and 0.97, respectively, in determining erosion susceptibility. Priority assessment based on the best model, the ensemble approach, indicated that the sub-basins SW3 and SW5 were found to have high sensitivity to the flood and soil erosion, respectively.

摘要

由于气候变化和人为干预，山区流域日益受到极端侵蚀和毁灭性洪水的挑战。危害测绘对于当地的预防政策制定、规划缓解行动以及适应极端情况至关重要。本研究提出了用于洪水和侵蚀敏感性绘图的新预测模型。此外，本研究详细阐述了在侵蚀和洪水敏感性方面对现有子流域进行优先排序。对广义线性模型 (GLM)、灵活判别分析 (FDA)、多元自适应回归样条 (MARS)、随机森林 (RF) 及其集合进行比较分析，以确保最高的预测性能。此外，根据最佳模型确定子流域对侵蚀和洪水的敏感性的优先级。结果表明，GLM、FDA、MARS、RF 和集成模型在模拟洪水敏感性时的曲线下面积 (AUC) 分别为 0.91、0.92、0.89、0.93 和 0.94。此外，在确定侵蚀敏感性时，GLM、FDA、MARS、RF 和集成模型的 AUC 分别为 0.93、0.92、0.89、0.96 和 0.97。基于最佳模型集成方法的优先级评估表明，发现子流域 SW3 和 SW5 分别对洪水和土壤侵蚀具有高敏感性。和集成模型在确定侵蚀敏感性方面的 AUC 分别为 0.93、0.92、0.89、0.96 和 0.97。基于最佳模型集成方法的优先级评估表明，发现子流域 SW3 和 SW5 分别对洪水和土壤侵蚀具有高敏感性。和集成模型在确定侵蚀敏感性方面的 AUC 分别为 0.93、0.92、0.89、0.96 和 0.97。基于最佳模型集成方法的优先级评估表明，发现子流域 SW3 和 SW5 分别对洪水和土壤侵蚀具有高敏感性。