利用基于地理信息系统的多标准决策分析(MCDA)对印度克什米尔-喜马拉雅山脉 1 号国道沿线的山体滑坡易发性进行评估

IF 2.3 Q2 REMOTE SENSING
Iftikhar Hussain Beigh, Syed Kaiser Bukhari
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引用次数: 0

摘要

本研究旨在通过基于地理信息系统的多标准决策分析,生成印度克什米尔喜马拉雅山脉 NH-1 公路沿线巴拉穆拉至乌里路段的易滑坡地图。为生成 AHP 矩阵,我们对滑坡致因因素进行了研究,包括坡度、海拔、坡面、曲率、与排水系统的距离、与道路的距离、与线状物的距离、地质、土地利用/土地覆盖和降雨量。该研究通过广泛的实地调查和其他二手数据来源,绘制并确定了 NH-1 沿线的活动滑坡。滑坡事件主要是岩崩和泥石流。根据专题层在滑坡事件中的重要性,采用萨蒂评分法对专题层进行了相对相关性评分。此外,还采用层次分析法(Analytic Hierarchy Process)对各专题图层的相对权重和属性进行归一化处理。此外,使用加权线性方法合并所有专题数据层,生成滑坡易发性地图。此外,所绘制的滑坡易发性地图被分为五类,即非常高(24.18%)、高(30.24%)、中等(28.61%)、低(15.28%)和非常低(1.69%)。研究显示,54.42%的区域属于高易感和极高易感区。同样,使用曲线下面积法计算出的最终滑坡易发区划图的整体模型准确率为 78.9%。此外,这项研究将有助于所研究地区的基础设施、地质环境和滑坡灾害规划。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Landslide susceptibility assessment using GIS-based multicriteria decision analysis (MCDA) along a part of National Highway-1, Kashmir- Himalayas, India

The current study aims at GIS-based multicriteria decision analysis to generate a landslide-susceptible map from Baramulla to Uri Road segment along NH-1, Kashmir Himalaya, India. The landslide causative factors examined to generate our AHP matrix are slope gradient, elevation, slope aspect, curvature, distance to drainage, distance to roads, distance to lineaments, geology, land use/land cover, and Rainfall. The study mapped and identified the active landslides along NH-1 through extensive field investigations and other secondary data sources. The landslide events were dominated by rockfall and debris slides. Based on their importance in landslide occurrences, the thematic layers were given relative relevance scores using Saaty's scale. Besides, the Analytic Hierarchy Process was employed to normalize the relative weights and attributes of the various thematic layers. In addition, all thematic data layers were combined using a weighted linear approach to generate the landslide susceptibility map. Furthermore, the resultant landslide susceptibility map was classed into five categories viz., very high (24.18%), high (30.24%), medium (28.61%), low (15.28%), and very low (1.69%). The study reveals that 54.42% of the area falls under the high and very high susceptible zones. Likewise, 78.9% of overall model accuracy of final landslide susceptible zonation map was computed using the area under curve method. Moreover, this study would aid infrastructural, geo-environmental, and landslide hazard planning in the studied region.

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来源期刊
Applied Geomatics
Applied Geomatics REMOTE SENSING-
CiteScore
5.40
自引率
3.70%
发文量
61
期刊介绍: Applied Geomatics (AGMJ) is the official journal of SIFET the Italian Society of Photogrammetry and Topography and covers all aspects and information on scientific and technical advances in the geomatics sciences. The Journal publishes innovative contributions in geomatics applications ranging from the integration of instruments, methodologies and technologies and their use in the environmental sciences, engineering and other natural sciences. The areas of interest include many research fields such as: remote sensing, close range and videometric photogrammetry, image analysis, digital mapping, land and geographic information systems, geographic information science, integrated geodesy, spatial data analysis, heritage recording; network adjustment and numerical processes. Furthermore, Applied Geomatics is open to articles from all areas of deformation measurements and analysis, structural engineering, mechanical engineering and all trends in earth and planetary survey science and space technology. The Journal also contains notices of conferences and international workshops, industry news, and information on new products. It provides a useful forum for professional and academic scientists involved in geomatics science and technology. Information on Open Research Funding and Support may be found here: https://www.springernature.com/gp/open-research/institutional-agreements
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