Department of Watershed Management Engineering, College of Natural Resources and Marine Science, Tarbiat Modares University, Noor, 46414-356, Iran.
Department of Tourism Management, Faculty of Humanities & Social Sciences, University of Mazandaran, Iran.
J Environ Manage. 2022 Dec 1;323:116261. doi: 10.1016/j.jenvman.2022.116261. Epub 2022 Sep 20.
Due to land-use and hydrology changes, people are constantly exposed to floods. The adverse impact of floods is greater on vulnerable populations that disproportionately inhabit flood-prone areas. This paper reports a comprehensive study on flood vulnerability of flood prone areas in residential areas of the Tajan watershed, Iran in two periods before 2006 and after 2006. Flood prone area were determined by the random forest (RF) and K-nearest neighbor (KNN) machine learning methods. To reduce time and cost, the vulnerability was assessed only in areas with very high flood hazard using 4 main criteria (social, policy, economic, infrastructure), 40 items, and 210 questionnaires across 40 villages. Independent t-test, Kruskal-Wallis, and paired t-test were used for statistical analysis of questionnaire data. The results of machine learning models (MLMs) showed that the RF model with AUC = 0.92% is more accurate in determining flood prone areas. The results of paired t-test showed that the three criteria of social (mean P1 = 2.97 and P2 = 3.35), infrastructure (mean P1 = 2.88 and P2 = 3.25), and policy (mean P1 = 3.02 and P2 = 3.50) had significant changes in both periods. The Kruskal-Wallis test also revealed the mean of all four criteria in both periods and all sub-watersheds, except three sub-watersheds 10 (Khalkhil village), 19 (Tellarem and Kerasp villages), and 23 (Dinehsar and Jafarabad), had a significant difference. The results of the t-test also showed a decrease in vulnerability in the second period (before 2006) compared to the first period (after 2006), so the number of sub-watersheds in the very high vulnerability class was more in the first period than in the second period. A vulnerability map was developed using three factors of risk zone area, area of each sub-watershed, and population of each sub-watershed.
由于土地利用和水文变化，人们不断面临洪水的威胁。洪水对居住在易受灾地区的弱势群体的不利影响更大。本文报告了对伊朗塔扬流域居民区易受灾地区洪水脆弱性的综合研究，研究时间为 2006 年之前和之后两个时期。易受灾地区是通过随机森林 (RF) 和 K-最近邻 (KNN) 机器学习方法确定的。为了减少时间和成本，仅在洪水高风险地区使用 4 个主要标准（社会、政策、经济、基础设施）、40 个项目和 40 个村庄的 210 份问卷评估脆弱性。独立 t 检验、克鲁斯卡尔-沃利斯检验和配对 t 检验用于问卷调查数据的统计分析。机器学习模型 (MLM) 的结果表明，AUC=0.92%的 RF 模型在确定易受灾地区方面更准确。配对 t 检验的结果表明，社会（均值 P1=2.97 和 P2=3.35）、基础设施（均值 P1=2.88 和 P2=3.25）和政策（均值 P1=3.02 和 P2=3.50）三个标准在两个时期都有显著变化。克鲁斯卡尔-沃利斯检验还显示，除了三个子流域 10（哈克希尔村）、19（泰勒雷姆和克拉萨村）和 23（迪内萨尔和贾法拉巴德）外，所有四个标准的平均值在两个时期和所有子流域都有显著差异。t 检验的结果还表明，与第一时期（2006 年之后）相比，第二时期（2006 年之前）脆弱性有所降低，因此，第一时期非常高脆弱性类别的子流域数量多于第二时期。利用风险区面积、每个子流域的面积和每个子流域的人口三个因素制作了一张脆弱性地图。
