Shobhit Chaturvedi , Naimish Bhatt , Vatsal Shah , Keval H. Jodhani , Dhruvesh Patel , Sudhir Kumar Singh
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The Land Suitability Index (LSI), derived using AHP, categorized the 124 sq. km study area into five suitability classes, with key factors influencing rankings: Landslide Proximity (weight: 0.162), Ground Slope (0.138), Land Use and Cover (0.122), Ground Elevation (0.114), and Road Proximity (0.095). From the Very High suitability zones, eight candidate sites were identified and ranked using VIKOR, with Kiargiri (score: 0.083) identified as the most suitable, followed by Baboloo (0.530), Karog (0.535), and Phayal Road (0.663). Sensitivity Analysis (SA) was conducted across five scenarios to account for possible variations in expert judgment, with the first three increasing beneficial weights (10 %, 15 %, and 20 %) and the last two decreasing beneficial weights (15 % and 20 %), proportionally adjusting non-beneficial weights. 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引用次数: 0
摘要
本文提出了一种新的综合地理信息系统-多准则决策(GIS-MCDM)框架,用于评估印度西姆拉这个快速城市化的山地站点的垃圾填埋场适宜性。结合遥感地理信息系统(RS-GIS)、层次分析法(AHP)和VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR)方法,该框架开发了垃圾填埋场适宜性分区图,并对潜在地点进行了排名。利用层次分析法得出的土地适宜性指数(LSI)对124平方公里的土地进行了分类。将研究区域划分为5个适宜性等级,主要影响因素为滑坡接近度(权重0.162)、地面坡度(权重0.138)、土地利用与覆盖(权重0.122)、地面高程(权重0.114)和道路接近度(权重0.095)。从非常高适宜性区域中,利用VIKOR对8个候选地点进行了识别和排序,其中Kiargiri(得分:0.083)被确定为最适宜的,其次是Baboloo(得分:0.530)、Karog(得分:0.535)和Phayal Road(得分:0.663)。敏感性分析(SA)在五种情况下进行,以解释专家判断的可能变化,前三种情况下有益权重增加(10%,15%和20%),后两种情况下有益权重减少(15%和20%),按比例调整非有益权重。SA证实了排名的一致性和稳健性,尽管权重配置不同,但Kiargiri(0.083)、Baboloo(0.530)、Karog(0.535)和Phayal Road(0.663)仍保持在前几位。这种方法为在丘陵市区选择堆填区提供了一个可靠的、适应性强的框架,支持废物管理、可持续发展和环境保护。
Landfill site selection in hilly terrains: An integrated RS-GIS approach with AHP and VIKOR
This paper presents a novel integrated Geographic Information System-Multi-Criteria Decision Making (GIS-MCDM) framework for evaluating landfill site suitability in Shimla, India, a rapidly urbanizing hill station. Combining Remote Sensing-Geographic Information Systems (RS-GIS) with the Analytical Hierarchy Process (AHP) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods, the framework developed a Landfill Suitability Zoning map and ranked potential sites. The Land Suitability Index (LSI), derived using AHP, categorized the 124 sq. km study area into five suitability classes, with key factors influencing rankings: Landslide Proximity (weight: 0.162), Ground Slope (0.138), Land Use and Cover (0.122), Ground Elevation (0.114), and Road Proximity (0.095). From the Very High suitability zones, eight candidate sites were identified and ranked using VIKOR, with Kiargiri (score: 0.083) identified as the most suitable, followed by Baboloo (0.530), Karog (0.535), and Phayal Road (0.663). Sensitivity Analysis (SA) was conducted across five scenarios to account for possible variations in expert judgment, with the first three increasing beneficial weights (10 %, 15 %, and 20 %) and the last two decreasing beneficial weights (15 % and 20 %), proportionally adjusting non-beneficial weights. The SA confirmed the consistency and robustness of the rankings, with Kiargiri (0.083), Baboloo (0.530), Karog (0.535), and Phayal Road (0.663) maintaining top positions despite varying weight configurations. This approach offers a reliable, adaptable framework for landfill site selection in hilly urban areas, supporting waste management, sustainable development and environmental conservation.