Abstract

The purpose of this study is to develop a reliable model for identification of flood susceptible areas. Three multi-criteria decision-making techniques, namely Analytical Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Attributive Border Approximation Area Comparison (MABAC) methods combined with weight of evidence (WOE) were used in Babolroud Watershed, Iran. First, 50 flood locations were identified, of which 35 (70%) locations were selected randomly for modelling, and 15 (30%) locations were used for validation. Using GIS with eight conditioning factors including rainfall, distance from rivers, slope, soil, geology, elevation, drainage density and land use, the flood susceptibility maps were prepared. Area under receiver operating characteristic curve (AUROC) for test data of AHP-WOE, TOPSIS-WOE-AHP and MABAC-WOE-AHP methods were 72.8%, 90.5% and 81.6%, respectively, which indicate the reasonable accuracy of models. High accuracy of the proposed new model (MABAC) clarifies its applicability for preventive measures.

摘要

本研究的目的是开发一个可靠的模型来识别洪水易发区。三种多准则决策技术，即层次分析法（AHP）、与理想解相似度排序技术（TOPSIS）和结合证据权重（WOE）的属性边界近似面积比较（MABAC）方法。用于伊朗巴博鲁德流域。首先，确定了 50 个洪水位置，其中随机选择 35 个（70%）位置进行建模，15 个（30%）位置用于验证。采用地理信息系统，结合降雨、河道距离、坡度、土壤、地质、海拔、排水密度和土地利用等8个条件因素，编制了洪水敏感性图。AHP-WOE 测试数据的受试者工作特征曲线下面积 (AUROC)，TOPSIS-WOE-AHP 和 MABAC-WOE-AHP 方法分别为 72.8%、90.5% 和 81.6%，表明模型的准确度合理。所提出的新模型（MABAC）的高精度阐明了其对预防措施的适用性。