Landslide susceptibility assessment (LSA) is a method used to reduce landslide vulnerability defined as landslide spatial prediction with the help of associative factors. The goal of the analysis is to forecast potential (2040, 2060, 2080, and 2100 AC) rainfall and land use and land cover (LULC) with the aid of the CSIRO-MK3.6.0 General circulation models (GCMs) climatic model and the dynamic conversion of land use and its effects (Dyna-CLUE) model. The purpose of this work is to produce landslide susceptibility map (LSM) of potential cycles correlated with expected rainfall and LULC data utilizing the binary logistic regression (BLR) models in the Upper Rangit River basin of eastern Himalayan region, India. Including rainfall and LULC total nineteen factors have been incorporated, these are mainly topographical, hydrological, geological, and environmental factors. A total 671 landslide locations have been mapped which divided randomly as training (70%) and validation (30%) datasets of LSM. Current (2018) LSM was validated by 30% of landslide inventory location and the result of area under curve of receiver operating characteristic (ROC) indicated that the LSM has 92.4% and 89.6% of the success and prediction rate respectively. To demonstrate the accuracy of the BLR landslide susceptibility (LS) model, this model was used to generated future LSMs based on projected of rainfall and LULC. The result of projected representative concentration pathway (RCP)-based rainfall depicted that the increasing trend of rainfall in the future period and the moderate to very high LS zones has also increased from 2040 to 2100. This study will be used to further study of landslide hazard (LH) studies with climatic approaches, and will also contribute to regional planning and development of current and future Upper Rangit River basin.

滑坡敏感性评估（LSA）是一种用于减少滑坡脆弱性的方法，定义为借助相关因素进行滑坡空间预测。分析的目标是借助CSIRO-MK3.6.0通用循环模型（GCM）气候模型和气候变化预测模型（2040、2060、2080和2100 AC）潜在的降雨，土地利用和土地覆盖（LULC）。土地利用的动态转换及其影响（Dyna-CLUE）模型。这项工作的目的是利用二值对数回归（BLR）模型在印度喜马拉雅东部地区的上朗吉特河流域绘制与预期降雨和LULC数据相关的潜在周期的滑坡敏感性图（LSM）。包括降雨和LULC在内的总共19个因素已被纳入，这些因素主要是地形，水文，地质，和环境因素。总共绘制了671个滑坡位置，将其随机分为LSM的训练（70％）和验证（30％）数据集。当前（2018年）的LSM已通过滑坡清单位置的30％进行了验证，接收器运行特征曲线下面积的结果表明ROS分别具有92.4％的成功率和89.6％的预测率。为了证明BLR滑坡易感性（LS）模型的准确性，该模型用于基于降雨和LULC的预测生成未来的LSM。预计的基于代表性集中路径（RCP）的降雨的结果表明，未来时期以及中度到极高LS区的降雨增加趋势也已从2040年增加到2100年。