Abstract：
Quantifying flood hazards by employing hydraulic/hydrodynamic models for flood risk mapping is a widely implemented non-structural flood management strategy. However, the unavailability of multi-domain and multi-dimensional input data and expensive computational resources limit its application in resource-constrained regions. The fifth and sixth IPCC assessment reports recommend including vulnerability and exposure components along with hazards for capturing risk on human-environment systems from natural and anthropogenic sources. In this context, the present study showcases a novel flood risk mapping approach that considers a combination of geomorphic flood descriptor (GFD)-based flood susceptibility and often neglected socio-economic vulnerability components. Three popular Machine Learning (ML) models, namely Decision Tree (DT), Random Forest (RF), and Gradient-boosted Decision Trees (GBDT), are evaluated for their abilities to combine digital terrain model-derived GFDs for quantifying flood susceptibility in a flood-prone district, Jagatsinghpur, located in the lower Mahanadi River basin, India. The area under receiver operating characteristics curve (AUC) along with Cohen\'s kappa are used to identify the best ML model. It is observed that the RF model performs better compared to the other two models on both training and testing datasets, with AUC score of 0.88 on each. The socio-economic vulnerability assessment follows an indicator-based approach by employing the Charnes-Cooper-Rhodes (CCR) model of Data Envelopment Analysis (DEA), an efficient non-parametric ranking method. It combines the district\'s relevant socio-economic sensitivity and adaptive capacity indicators. The flood risk classes at the most refined administrative scale, i.e., village level, are determined with the Jenks natural breaks algorithm using flood susceptibility and socio-economic vulnerability scores estimated by the RF and CCR-DEA models, respectively. It was observed that >40 % of the villages spread over Jagatsinghpur face high and very high flood risk. The proposed novel framework is generic and can be used to derive a wide variety of flood susceptibility, vulnerability, and subsequently risk maps under a data-constrained scenario. Furthermore, since this approach is relatively data and computationally parsimonious, it can be easily implemented over large regions. The exhaustive flood maps will facilitate effective flood control and floodplain planning.
摘要：
通过采用水力/水动力模型进行洪水风险测绘来量化洪水灾害是一种广泛实施的非结构性洪水管理策略。然而,多域和多维输入数据的不可用性以及昂贵的计算资源限制了其在资源受限区域的应用。IPCC的第五和第六次评估报告建议包括脆弱性和暴露成分以及危害，以从自然和人为来源捕获人类环境系统的风险。在这种情况下，本研究展示了一种新颖的洪水风险制图方法，该方法考虑了基于地貌洪水描述符（GFD）的洪水敏感性和经常被忽视的社会经济脆弱性成分的组合。三种流行的机器学习（ML）模型，即决策树(DT)，随机森林(RF),和梯度增强决策树(GBDT)，评估了它们结合数字地形模型衍生的GFD以量化易发洪水地区的洪水敏感性的能力，Jagatsinghpur,位于马哈纳迪河下游流域，印度。使用接收器工作特征曲线下面积(AUC)以及Cohen'sκ来确定最佳ML模型。观察到RF模型在训练和测试数据集上与其他两个模型相比表现更好。AUC得分为0.88。社会经济脆弱性评估采用基于指标的方法，采用数据包络分析（DEA）的Charnes-Cooper-Rhodes（CCR）模型，一种有效的非参数排序方法。它结合了该地区的相关社会经济敏感性和适应能力指标。最精细行政尺度的洪水风险等级，即，村级,使用Jenks自然中断算法，使用RF和CCR-DEA模型估计的洪水敏感性和社会经济脆弱性得分来确定，分别。据观察，分布在Jagatsinghpur的村庄中有40％以上的村庄面临着很高的洪水风险。所提出的新框架是通用的，可用于推导各种洪水敏感性，脆弱性,以及随后在数据受限场景下的风险图。此外，由于这种方法是相对数据和计算简约的，它可以很容易地在大地区实施。详尽的洪水图将有助于有效的防洪和洪泛区规划。
