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首页> 外文期刊>Environment, development and sustainability >Application of statistical probabilistic methods in landslide susceptibility assessment in Kurseong and its surrounding area of Darjeeling Himalayan, India: RS-GIS approach
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Application of statistical probabilistic methods in landslide susceptibility assessment in Kurseong and its surrounding area of Darjeeling Himalayan, India: RS-GIS approach

机译:统计概率方法在印度大吉岭喜马拉雅大鸡及其周边地区山体滑坡敏感性评估中的应用:RS-GIS方法

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摘要

Kurseong and its surrounding areas are frequently affected by the landslide which causes huge loss of properties and lives. The landslide susceptibility maps can play an important role in human development and sustainable environment management in Darjeeling Himalayan. The present research focused on preparing the effective landslide susceptible models using different statistical probabilistic methods, namely landslide nominal susceptibility factor (LNSF), information value (InfoVal) and certainty factor (CF) models. Experiments have been carried out in Kurseong region, a part of Darjeeling Himalaya as a field of this research. Landslide sites were identified from previous records through extensive field survey and Google pro satellite imagery. Totally, 273 landslide sites were compiled and prepared the inventory map. Out of 273 landslides, 70% were used for training and 30% were used for validating the models. Seventeen landslide conditioning factors were selected for modeling landslide susceptibility, i.e., elevation, aspect, slope degree, rainfall, geological structure, geomorphologic division, lineament, land use/land cover, distance to roads, earthquake zone, soil texture, soil depth, normalized difference vegetation index, drainage density, stream power index and topographic wetness index. The produced susceptibility maps were validated using the receiver's operating characteristic (ROC) curves. The prediction accuracy of LNSF, InfoVal and CF models as per ROC are 80.78%, 82.91% and 86.13%, respectively. The CF achieved the highest accuracy (86.13%), while the LNSF produced the lowest ROC value (80.78%). However, the comparison of the produced landslide maps revealed that all the applied models have good precision for studying susceptibility of landslide in Kurseong and its surrounding of Darjeeling Himalaya, India. The findings of current study can be supportive for the mitigation of landslide risk in the Kurseong range as well as the surrounding comparable areas having same geoenvironmental conditions.
机译:Kurseong及其周边地区经常受到山体滑坡的影响,导致巨大的物业和生活损失。滑坡易感性地图可以在大吉岭喜马拉雅达尔的人类发展和可持续环境管理中发挥重要作用。本研究专注于使用不同统计概率方法的有效山体滑坡易感模型,即滑坡标称敏感因子(LNSF),信息值(Infoval)和确定性因子(CF)模型。实验已经在Kurseong地区进行,是Darjeeling Himalaya的一部分作为这项研究的领域。通过广泛的现场调查和Google Pro卫星图像从以前的记录中确定了Landslide网站。完全,编制了273个滑坡网站并准备了库存地图。在273个山体滑坡中,70%用于培训,30%用于验证模型。选择了十七个滑坡调节因子,用于造型滑坡易感性,即高度,方面,坡度,降雨,地质结构,地貌划分,衬里,土地利用/陆地覆盖,距离道路,地震区,土壤质地,土壤深度,归一化差异植被指数,排水密度,流功率指数和地形湿度指数。使用接收器的操作特征(ROC)曲线验证产生的易感性图。根据ROC的LNSF,INFOROM和CF型号的预测准确性分别为80.78%,82.91%和86.13%。 CF实现最高精度(86.13%),而LNSF产生最低的ROC值(80.78%)。然而,所生产的滑坡地图的比较透露,所有应用的模型都具有良好的精确度,用于研究Kurseong山体滑坡的易感性及其印度大吉岭喜马拉雅山的周围。目前研究的调查结果可以支持缓解Kurseong范围的滑坡风险以及具有相同地理环境条件的周围的可比较区域。

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