基于ahp信息加权法的东爪哇Andungbiru流域山洪易发度制图

Devi Ratna Handini, E. Hidayah, G. Halik
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引用次数: 6

摘要

山洪暴发是由暴雨和强雷暴引起的最常见的自然灾害之一,会给基础设施和农业造成社会和经济损失。因此,本研究旨在利用地理信息系统(GIS)技术和统计分析,绘制Pekalen流域的山洪潜在易感性(FFPS)图,以降低洪水发生的风险。运用层次分析法(AHP)对权重评价方法进行了专家意见和经验的分析。此外,利用概率统计方法和GIS对Probolinggo村Andungbiru Pekalen流域的山洪暴发区进行了研究。本研究采用高程、坡度、水流功率指数和地形湿度指数等地貌因子,分辨率为30 m。专题地图的比例尺为土地利用、河流密度、到河流的距离、降雨量、地质情况,比例为1:25.000。图像处理使用Landsat 8 30 m x 30 m分辨率图像,如归一化植被指数。结果表明,FFPS模型图的指标值分别为低8%、低23%、中27%、中高26%、高13%和极高2%。下一阶段的建模分析导致使用曲线下面积(AUC)的统计接受者工作特征曲线(ROC)进行验证,其值为90.15。综上所述,与河流的距离、土地利用和坡度是引发山洪暴发的重要因素。关键词:AHP-weighted;信息内容;FFSP;GIS;Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember本作品采用知识共享署名-共享a like 4.0国际许可协议
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Flash Flood Susceptibility Mapping at Andungbiru Watershed, East Java Using AHP-Information Weighted Method
Flash floods are among the most frequent natural disasters caused by heavy rain associated with a severe thunderstorm, which leads to social and economic losses in infrastructure and agriculture. Therefore, this research aims to map flash flood potential susceptibility (FFPS) in the Pekalen watershed, using Geographic Information System (GIS) technology and statistical analysis to reduce the risk of flooding. The opinion and experience of an expert on the weight assessment method were carried out using the Analytical Hierarchy Process (AHP). Furthermore, the probability statistical methods and GIS were used in flash flood areas in the Pekalen watershed in Andungbiru, Probolinggo village. This study was carried out using geomorphological factors, namely elevation, slope, stream power index, and topographic wetness index, with a resolution of 30 m. Thematic map scale of the land use, river density, distance to the river, rainfall, and geology is in the ratio of is in a ratio of 1:25.000. Imagery processing was carried out using Landsat 8 30 m x 30 m resolution imagery, such as the Normalized Difference Vegetation Index. The result showed that the model map of FFPS obtained low 8%, low 23%, moderate 27%, moderate to high 26%, high 13%, and very high 2% index values. The next stage of modeling analysis led to validation using statistic receiver operating Characteristic Curve (ROC) of area Under Curve (AUC) with a value of 90.15. In conclusion, the factors that significantly trigger flash floods are distance to the river, land use, and slope.   Keywords: AHP-weighted; information content; FFSP; GIS; Geomorphology Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember   This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License
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