当前位置: X-MOL 学术Geomat Nat. Hazards Risk › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Flood susceptibility mapping using an improved analytic network process with statistical models
Geomatics, Natural Hazards and Risk ( IF 4.5 ) Pub Date : 2020-01-01 , DOI: 10.1080/19475705.2020.1836036
Peyman Yariyan 1 , Mohammadtaghi Avand 2 , Rahim Ali Abbaspour 3 , Ali Torabi Haghighi 4 , Romulus Costache 5, 6 , Omid Ghorbanzadeh 7 , Saeid Janizadeh 2 , Thomas Blaschke 7
Affiliation  

Abstract Flooding is a natural disaster that causes considerable damage to different sectors and severely affects economic and social activities. The city of Saqqez in Iran is susceptible to flooding due to its specific environmental characteristics. Therefore, susceptibility and vulnerability mapping are essential for comprehensive management to reduce the harmful effects of flooding. The primary purpose of this study is to combine the Analytic Network Process (ANP) decision-making method and the statistical models of Frequency Ratio (FR), Evidential Belief Function (EBF), and Ordered Weight Average (OWA) for flood susceptibility mapping in Saqqez City in Kurdistan Province, Iran. The frequency ratio method was used instead of expert opinions to weight the criteria in the ANP. The ten factors influencing flood susceptibility in the study area are slope, rainfall, slope length, topographic wetness index, slope aspect, altitude, curvature, distance from river, geology, and land use/land cover. We identified 42 flood points in the area, 70% of which was used for modelling, and the remaining 30% was used to validate the models. The Receiver Operating Characteristic (ROC) curve was used to evaluate the results. The area under the curve obtained from the ROC curve indicates a superior performance of the ANP and EBF hybrid model (ANP-EBF) with 95.1% efficiency compared to the combination of ANP and FR (ANP-FR) with 91% and ANP and OWA (ANP-OWA) with 89.6% efficiency.

中文翻译:

使用改进的分析网络过程和统计模型绘制洪水敏感性图

摘要 洪涝灾害是一种对不同部门造成重大破坏、严重影响经济社会活动的自然灾害。伊朗的萨奎兹市由于其特定的环境特征而容易发生洪水。因此,敏感性和脆弱性绘图对于综合管理以减少洪水的有害影响至关重要。本研究的主要目的是将分析网络过程 (ANP) 决策方法与频率比 (FR)、证据置信函数 (EBF) 和有序加权平均 (OWA) 的统计模型相结合,用于洪水敏感性映射。伊朗库尔德斯坦省的萨奎兹市。使用频率比率方法代替专家意见来对 ANP 中的标准进行加权。影响研究区洪水敏感性的十个因素是坡度、降雨量、坡长、地形湿度指数、坡向、海拔、曲率、与河流的距离、地质和土地利用/土地覆盖。我们在该地区确定了 42 个洪水点,其中 70% 用于建模,其余 30% 用于验证模型。接收者操作特征 (ROC) 曲线用于评估结果。从 ROC 曲线获得的曲线下面积表明 ANP 和 EBF 混合模型 (ANP-EBF) 的性能优越,效率为 95.1%,与 ANP 和 FR 的组合 (ANP-FR) 相比,效率为 91%,ANP 和 OWA (ANP-OWA) 效率为 89.6%。我们在该地区确定了 42 个洪水点,其中 70% 用于建模,其余 30% 用于验证模型。接收者操作特征 (ROC) 曲线用于评估结果。从 ROC 曲线获得的曲线下面积表明 ANP 和 EBF 混合模型 (ANP-EBF) 的性能优越,效率为 95.1%,与 ANP 和 FR 的组合 (ANP-FR) 相比,效率为 91%,ANP 和 OWA (ANP-OWA) 效率为 89.6%。我们在该地区确定了 42 个洪水点,其中 70% 用于建模,其余 30% 用于验证模型。接收者操作特征 (ROC) 曲线用于评估结果。从 ROC 曲线获得的曲线下面积表明 ANP 和 EBF 混合模型 (ANP-EBF) 的性能优越,效率为 95.1%,与 ANP 和 FR 的组合 (ANP-FR) 相比,效率为 91%,ANP 和 OWA (ANP-OWA) 效率为 89.6%。
更新日期:2020-01-01
down
wechat
bug