当前位置: X-MOL 学术Geocarto Int. › 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.)
Application of geographically weighted principal component analysis and fuzzy approach for unsupervised landslide susceptibility mapping on Gish River Basin, India
Geocarto International ( IF 3.8 ) Pub Date : 2020-06-19 , DOI: 10.1080/10106049.2020.1778105
Tirthankar Basu 1 , Arijit Das 1 , Swades Pal 1
Affiliation  

Abstract

The reason behind the landslides in Darjeeling Himalaya varies from place to place. Seepage of rainwater through fragile lithological structure initiates shallow landslides, while modification of slope due to unscientific anthropogenic activities led to the occurrence of deep-seated landslides. Besides this, this region does not possess any proper landslide inventory at the micro-scale. All of these together pose a serious challenge to the researchers to develop any landslide susceptibility map for this region. From this end, this study has tried to develop a way of unsupervised landslide susceptibility mapping by applying geographically weighted principal component analysis and fuzzy approach. For this, 12 indicators are selected and categorized into three categories (actuate, lithological and topographic). The final output shows that 23.82 sq. km area lies in the very high landslide susceptible zone. Validation outputs suggest that the proposed methodology with >87% predicted rate is suitable for landslide susceptibility mapping.



中文翻译:

地理加权主成分分析和模糊方法在印度吉什河流域无监督滑坡敏感性绘图中的应用

摘要

大吉岭喜马拉雅山体滑坡背后的原因因地而异。雨水通过脆弱的岩性结构渗流引发浅层滑坡,而由于不科学的人为活动对坡度的改造导致深部滑坡的发生。除此之外,该地区没有任何适当的微观滑坡清单。所有这些共同构成了研究人员为该地区绘制任何滑坡敏感性图的严峻挑战。为此,本研究试图通过应用地理加权主成分分析和模糊方法来开发一种无监督滑坡敏感性绘图的方法。为此,选择了 12 个指标并将其分为三类(驱动、岩性和地形)。最终输出显示 23.82 平方米。千米区域位于非常高的滑坡易发区。验证结果表明,所提出的方法具有 >87% 的预测率,适用于滑坡敏感性绘图。

更新日期:2020-06-19
down
wechat
bug