Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia.
Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
Environ Res. 2024 Mar 15;245:118042. doi: 10.1016/j.envres.2023.118042. Epub 2023 Dec 29.
Coastal areas are at a higher risk of flooding, and novel changes in the climate are induced to raise the sea level. Flood acceleration and frequency have increased recently because of unplanned infrastructural conveniences and anthropogenic activities. Therefore, the assessment of flood susceptibility mapping is considered the most significant flood management model. In this paper, flood susceptibility identification is performed by applying the innovative Multi-criteria decision-making model (MCDM) called Analytical Hierarchy Process (AHP) by ensembles with Support vector machine (AHP-SVM) and Decision Tree (AHP-DT). This model combines two Representation concentration pathway (RCP) scenarios such as RCP 2.6 & RCP 8.5. The factors influencing the coastal flooding in Bandar Abbas, Iran, identified through Flood susceptibility mapping. Multi-criteria decision-making (MCDM) has been applied to evaluate the Coastal flood conditioning factors, and ensemble machine learning (ML) approaches are employed for Coastal risk factor (CRF) prediction and classification. The statistical variances are measured through Friedman and Wilcoxon signed rank tests and statistical metrics such as Accuracy, sensitivity, and specificity. Among the models, AHP-DT obtained an improved AUC value of ROC as 0.95. After applying the ML models, the northern and western park of Raidak Basin River recognises very low and low flood susceptibility because of their topographic characteristics. The eastern part of the middle section fell very high and high CFSM. Observed from this result analysis, the people living nearer to the coastline are distributed by the low to medium exposure in the region of the west and middle of the considered study area. The results of this study can help decision-makers take necessary risk reduction approaches in the high-risk flooding zones of the coastal system.
沿海地区面临更高的洪水风险，气候的新变化导致海平面上升。由于基础设施规划不当和人为活动，洪水加速和频率最近有所增加。因此，洪水易感性评估被认为是最重要的洪水管理模型。本文通过应用创新的多标准决策模型（MCDM），即层次分析法（AHP）与支持向量机（AHP-SVM）和决策树（AHP-DT）的集成，来进行洪水易感性识别。该模型结合了两种代表浓度途径（RCP）情景，即 RCP2.6 和 RCP8.5。通过洪水易感性图，确定了伊朗班达尔阿巴斯沿海洪水的影响因素。多标准决策（MCDM）已应用于评估沿海洪水影响因素，并且集成机器学习（ML）方法用于预测和分类沿海风险因素（CRF）。通过 Friedman 和 Wilcoxon 符号秩检验和准确性、敏感性和特异性等统计指标来测量统计方差。在这些模型中，AHP-DT 获得了改进的 ROC 曲线下面积（AUC）值为 0.95。在应用 ML 模型之后，由于地形特征，Raidak 流域北部和西部公园的洪水易感性被认为非常低和低。中部分段的东部则被认为是非常高和高 CFSM。从这个结果分析来看，生活在靠近海岸线的人在考虑研究区域的西部和中部地区，分布在低到中等暴露风险区域。这项研究的结果可以帮助决策者在沿海系统的高风险洪水区域采取必要的风险降低措施。
