TOPSIS 方法:使用 Excel 和 GIS 计算滑坡易发性

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES MethodsX Pub Date : 2024-10-17 DOI:10.1016/j.mex.2024.103005
Jonmenjoy Barman , Brototi Biswas , Syed Sadath Ali , Mohamed Zhran
{"title":"TOPSIS 方法:使用 Excel 和 GIS 计算滑坡易发性","authors":"Jonmenjoy Barman ,&nbsp;Brototi Biswas ,&nbsp;Syed Sadath Ali ,&nbsp;Mohamed Zhran","doi":"10.1016/j.mex.2024.103005","DOIUrl":null,"url":null,"abstract":"<div><div>The current study introduced Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to enhance landslide susceptibility. It determines the relative distance of each alternative from the ideal best and ideal worst value. The ArcGIS environment was used to prepare eleven landslide conditioning factors, while raster values were extracted for the decision matrix preparation. We utilized subjective expert judgment to create a weighted matrix that considers the roles of each conditioning component. In addition, a Euclidean distance was measured from each alternative to the ideal best and worst values. The relative closeness value (R<sub>i</sub>) has been used to prepare the landslide susceptibility index by the inverse distance weighting (IDW) interpolation. Furthermore, the precision of the landslide susceptibility was justified by area under curve-receiver operating characteristic (AUC-ROC) which was 0.987. Hence, multi-criteria decision-making (MCDM) techniques like the TOPSIS method are very useful for natural hazard mapping.</div><div><ul><li><span>•</span><span><div>The simplified TOPSIS approach described by Hwang and Yoon (1981) is applied in this study. The criteria have been categorized and assigned weights based on expert judgment and previously published material.</div></span></li><li><span>•</span><span><div>The TOPSIS approach and GIS integration has significantly enhanced the creation of a landslide susceptibility map for a sensitive area.</div></span></li><li><span>•</span><span><div>The method is easiest and suitable for short term operation research.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"13 ","pages":"Article 103005"},"PeriodicalIF":1.6000,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The TOPSIS method: Figuring the landslide susceptibility using Excel and GIS\",\"authors\":\"Jonmenjoy Barman ,&nbsp;Brototi Biswas ,&nbsp;Syed Sadath Ali ,&nbsp;Mohamed Zhran\",\"doi\":\"10.1016/j.mex.2024.103005\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The current study introduced Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to enhance landslide susceptibility. It determines the relative distance of each alternative from the ideal best and ideal worst value. The ArcGIS environment was used to prepare eleven landslide conditioning factors, while raster values were extracted for the decision matrix preparation. We utilized subjective expert judgment to create a weighted matrix that considers the roles of each conditioning component. In addition, a Euclidean distance was measured from each alternative to the ideal best and worst values. The relative closeness value (R<sub>i</sub>) has been used to prepare the landslide susceptibility index by the inverse distance weighting (IDW) interpolation. Furthermore, the precision of the landslide susceptibility was justified by area under curve-receiver operating characteristic (AUC-ROC) which was 0.987. Hence, multi-criteria decision-making (MCDM) techniques like the TOPSIS method are very useful for natural hazard mapping.</div><div><ul><li><span>•</span><span><div>The simplified TOPSIS approach described by Hwang and Yoon (1981) is applied in this study. The criteria have been categorized and assigned weights based on expert judgment and previously published material.</div></span></li><li><span>•</span><span><div>The TOPSIS approach and GIS integration has significantly enhanced the creation of a landslide susceptibility map for a sensitive area.</div></span></li><li><span>•</span><span><div>The method is easiest and suitable for short term operation research.</div></span></li></ul></div></div>\",\"PeriodicalId\":18446,\"journal\":{\"name\":\"MethodsX\",\"volume\":\"13 \",\"pages\":\"Article 103005\"},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-10-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"MethodsX\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2215016124004564\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"MethodsX","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2215016124004564","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

目前的研究引入了 "与理想方案相似度排序偏好技术"(TOPSIS),以提高滑坡的易发性。它确定了每个备选方案与理想最佳值和理想最坏值的相对距离。我们使用 ArcGIS 环境编制了 11 个滑坡条件因子,并提取了栅格值用于编制决策矩阵。我们利用专家的主观判断创建了一个加权矩阵,该矩阵考虑了每个调节因素的作用。此外,我们还测量了每个备选方案与理想的最佳值和最坏值之间的欧氏距离。通过反距离加权(IDW)插值法,利用相对接近值(Ri)来编制滑坡易感性指数。此外,曲线下面积-接收器工作特征(AUC-ROC)为 0.987,证明了滑坡易感性的精确性。因此,像 TOPSIS 方法这样的多标准决策(MCDM)技术对于绘制自然灾害地图非常有用。本研究采用了 Hwang 和 Yoon(1981 年)描述的简化 TOPSIS 方法,并根据专家判断和以前发表的资料对标准进行了分类和权重分配。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
The TOPSIS method: Figuring the landslide susceptibility using Excel and GIS
The current study introduced Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to enhance landslide susceptibility. It determines the relative distance of each alternative from the ideal best and ideal worst value. The ArcGIS environment was used to prepare eleven landslide conditioning factors, while raster values were extracted for the decision matrix preparation. We utilized subjective expert judgment to create a weighted matrix that considers the roles of each conditioning component. In addition, a Euclidean distance was measured from each alternative to the ideal best and worst values. The relative closeness value (Ri) has been used to prepare the landslide susceptibility index by the inverse distance weighting (IDW) interpolation. Furthermore, the precision of the landslide susceptibility was justified by area under curve-receiver operating characteristic (AUC-ROC) which was 0.987. Hence, multi-criteria decision-making (MCDM) techniques like the TOPSIS method are very useful for natural hazard mapping.
  • The simplified TOPSIS approach described by Hwang and Yoon (1981) is applied in this study. The criteria have been categorized and assigned weights based on expert judgment and previously published material.
  • The TOPSIS approach and GIS integration has significantly enhanced the creation of a landslide susceptibility map for a sensitive area.
  • The method is easiest and suitable for short term operation research.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
MethodsX
MethodsX Health Professions-Medical Laboratory Technology
CiteScore
3.60
自引率
5.30%
发文量
314
审稿时长
7 weeks
期刊介绍:
期刊最新文献
Experimental optimization for synthesis of cerium-doped titanium dioxide nanoparticles by modified sol-gel process Correction methods and applications of ERT in complex terrain A method to enhance privacy preservation in cloud storage through a three-layer scheme for computational intelligence in fog computing Method for measuring the transpiration resistance of fruit and vegetables Deep learning-based classification of alfalfa varieties: A comparative study using a custom leaf image dataset
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1