Abstract：
Quantifying flood risks through a cascade of hydraulic-cum-hydrodynamic modelling is data-intensive and computationally demanding- a major constraint for economically struggling and data-scarce low and middle-income nations. Under such circumstances, geomorphic flood descriptors (GFDs), that encompass the hidden characteristics of flood propensity may assist in developing a nuanced understanding of flood risk management. In line with this, the present study proposes a novel framework for estimating flood hazard and population exposure by leveraging GFDs and Machine Learning (ML) models over severely flood-prone Ganga basin. The study incorporates SHapley Additive exPlanations (SHAP) values in flood hazard modeling to justify the degree of influence of each GFD on the simulated floodplain maps. A set of 15 relevant GFDs derived from high-resolution CartoDEM are forced to five state-of-the-art ML models; AdaBoost, Random Forest, GBDT, XGBoost, and CatBoost, for predicting flood extents and depths. To enumerate the performance of ML models, a set of twelve statistical metrics are considered. Our result indicates a superior performance of XGBoost (κ = 0.72 and KGE = 82%) over other ML models in flood extent and flood depth prediction, resulting in about 47% of the population exposure to high-flood risks. The SHAP summary plots reveal a pre-dominance of Height Above Nearest Drainage during flood depth prediction. The study contributes significantly in comprehending our understanding of catchment characteristics and its influence in the process of sustainable disaster risk reduction. The results obtained from the study provide valuable recommendations for efficient flood management and mitigation strategies, especially over global data-scarce flood-prone basins.
摘要：
通过一系列水力和水动力模型量化洪水风险是数据密集型和计算需求的，这是经济困难和数据稀缺的中低收入国家的主要制约因素。在这种情况下,地貌洪水描述符(GFD)，包含洪水倾向的隐藏特征可能有助于发展对洪水风险管理的细致入微的理解。与此相符,本研究提出了一个新的框架，通过利用GFD和机器学习（ML）模型在严重洪水易发的恒河流域估计洪水灾害和人口暴露。该研究在洪水灾害模型中纳入了SHapley附加扩张（SHAP）值，以证明每个GFD对模拟洪泛区图的影响程度。来自高分辨率CartoDEM的一组15个相关GFD被强制使用五种最先进的ML模型；AdaBoost，随机森林,GBDT,XGBoost,和CatBoost,预测洪水的范围和深度。要枚举ML模型的性能，一组十二个统计指标被考虑。我们的结果表明，XGBoost（κ=0.72和KGE=82％）在洪水范围和洪水深度预测方面优于其他ML模型，导致约47%的人口面临高洪水风险。SHAP摘要图揭示了洪水深度预测过程中最近排水高度的优势。该研究有助于理解我们对流域特征及其在可持续减少灾害风险过程中的影响的理解。从研究中获得的结果为有效的洪水管理和缓解策略提供了有价值的建议，特别是在全球数据稀缺的洪水易发盆地上。
