In the present study, landslide susceptibility mapping in the Goriganga valley, Kumaun Himalaya has been carried out using bivariate and multivariate approaches. Bivariate analysis includes Yule’s Coefficient, Frequency Ratio, Information Value, and Weight of Evidence, whereas multivariate analysis used is Artificial Neural Network. The input data used for this purpose include an inventory of 421 active landslides and twelve possible causative factors of landslides like lithology, slope angle, slope aspect, elevation, curvature-plan, curvature-profile, distance to drainage, road & thrusts, land use and land cover. Rainfall pattern and the peak ground acceleration (PGA) of area were also considered for the analysis. Using both the bivariate and multivariate methods, it has been observed that ~20–25% of the study area lies in the high and very high landslide susceptible zones, whereas ~50–63% of the study area lies in the low and very low susceptible zone. The high and very high landlslide susceptible zones are mainly confined in the Lesser Himalaya and along the Goriganga River, whereas low and very low susceptible zones are mainly located in the Higher Himalaya, Tethys Himalaya, and the higher elevation of the Lesser Himalaya. Further all the four bivariate methods indicate success rate between 84 and 86%, and the prediction rate between 80 and 86%, and increase with the application of the ANN.

在本研究中，使用双变量和多变量方法对库曼喜马拉雅山戈里甘加山谷的滑坡敏感性绘图进行了绘制。双变量分析包括 Yule 系数、频率比、信息值和证据权重，而使用的多变量分析是人工神经网络。用于此目的的输入数据包括 421 个活动滑坡的清单和十二个可能的滑坡成因，如岩性、坡角、坡向、高程、曲率平面、曲率剖面、排水距离、道路和冲断、土地利用和土地覆盖。分析中还考虑了降雨模式和区域的峰值地面加速度 (PGA)。使用二元和多元方法，据观察，大约 20-25% 的研究区域位于高和非常高的滑坡易感区，而大约 50-63% 的研究区域位于低和非常低的易感区。高、特高滑坡易感带主要分布在小喜马拉雅山和戈里甘加河沿岸，低和特低易感带主要分布在高喜马拉雅山、特提斯喜马拉雅山和小喜马拉雅高海拔地区。此外，所有四种双变量方法均表明成功率在 84% 和 86% 之间，预测率在 80% 和 86% 之间，并且随着 ANN 的应用而增加。高、特高滑坡易感带主要分布在小喜马拉雅山和戈里甘加河沿岸，低和特低易感带主要分布在高喜马拉雅山、特提斯喜马拉雅山和小喜马拉雅高海拔地区。此外，所有四种双变量方法均表明成功率在 84% 和 86% 之间，预测率在 80% 和 86% 之间，并且随着 ANN 的应用而增加。高、特高滑坡易感带主要分布在小喜马拉雅山和戈里甘加河沿岸，低和特低易感带主要分布在高喜马拉雅山、特提斯喜马拉雅山和小喜马拉雅高海拔地区。此外，四种双变量方法均表明成功率在 84% 到 86% 之间，预测率在 80% 到 86% 之间，并且随着 ANN 的应用而增加。