基于gis的水文地貌因子多准则决策分析在孟加拉国山洪易感性制图中的应用

IF 8.7 Q1 Environmental Science
Raihan Riaz , Md. Mohiuddin
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引用次数: 0

摘要

山洪暴发是最常见的自然灾害之一,对孟加拉国Haor(湿地)地区的家园、农作物、基础设施、道路网络、通信和自然环境造成致命破坏。研究的目的是确定11个水文地貌驱动因素,即高程、坡度、坡向、降雨量、土地利用和土地覆盖(LULC)、岩性、土壤类型、地形湿度指数(TWI)、归一化植被指数(NDVI)、与河流的距离和排水密度,这些因素正在为洪水易发地区的测绘进行探索。本研究利用ArcGIS 10.8中基于多准则决策分析(MCDA)的交互决策方法——层次分析法(AHP)和网络分析法(ANP),绘制了山洪易感性图。研究结果表明,7个豪尔区对洪涝灾害的易感性存在显著差异。由于ANP和AHP的结果,Haor地区更大比例的中度易受洪水影响(8685.09-9275.15平方)。,而35.34% - 38.32%(7069.70-7668.67平方公里)。公里)的地区易受水浸影响。此外,在Haor地区东北部确定了200个洪水点,其中140个(70%)随机选择的洪水用于训练,其余60个(30%)用于验证目的。验证结果表明,AHP模型的预测准确率(AUROC = 92.1%)高于ANP模型(AUROC = 88.5%)。因此,研究结果可以帮助研究人员、学者、政策制定者和规划者制定可持续的洪水缓解策略,特别是在Haor地区。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of GIS-based multi-criteria decision analysis of hydro-geomorphological factors for flash flood susceptibility mapping in Bangladesh
Flash floods are one of the most prevalent natural disasters, triggering deadly damage to homesteads, crops, infrastructure, road networks, communications, and the natural environment in the Haor (Wetland) region of Bangladesh. The purpose of the study aims to identify eleven (11) hydro-geomorphological driving factors, namely elevation, slope, aspect, rainfall, land use and land cover (LULC), lithology, soil type, topographic wetness index (TWI), Normalized Difference Vegetation Index (NDVI), distance from the river, and drainage density, which are being explored for mapping flood-prone areas. This research has produced a flash flood susceptibility map using the Analytical Hierarchy Process (AHP) and Analytical Network Process (ANP), which are interactive decision-making approaches under multi-criteria decision analysis (MCDA) in ArcGIS 10.8. The findings of this study showed that the susceptibility to flood hazards differs significantly among the seven Haor districts. As a result of the ANP and AHP, a more significant proportion of the Haor region is moderately susceptible to flooding (8685.09–9275.15 sq. km.), whereas 35.34 %–38.32 % (7069.70–7668.67 sq. km.) accounts for high susceptible to flooding. Furthermore, 200 flood locations were identified in the northeast Haor region, where 140 (70 %) randomly selected floods were used for training, and the remaining 60 (30 %) were employed for validation purposes. The validation results showed that the AHP model had greater prediction accuracy (the area under the receiver operating curve (AUROC) = 92.1 %) than the ANP (AUROC = 88.5 %) model. Therefore, the study findings can be helpful for researchers, academics, policymakers, and planners for sustainable flood mitigation strategies, particularly in Haor areas.
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来源期刊
Water Cycle
Water Cycle Engineering-Engineering (miscellaneous)
CiteScore
9.20
自引率
0.00%
发文量
20
审稿时长
45 days
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