Landslide is considered as one of the most severe threats to human life and property in the hilly areas of the world. The number of landslides and the level of damage across the globe has been increasing over time. Therefore, landslide management is essential to maintain the natural and socio-economic dynamics of the hilly region. Rorachu river basin is one of the most landslide-prone areas of the Sikkim selected for the present study. The prime goal of the study is to prepare landslide susceptibility maps (LSMs) using computer-based advanced machine learning techniques and compare the performance of the models. To properly understand the existing spatial relation with the landslide, twenty factors, including triggering and causative factors, were selected. A deep learning algorithm viz. convolutional neural network model (CNN) and three popular machine learning techniques, i.e., random forest model (RF), artificial neural network model (ANN), and bagging model, were employed to prepare the LSMs. Two separate datasets including training and validation were designed by randomly taken landslide and non-landslide points. A ratio of 70:30 was considered for the selection of both training and validation points. Multicollinearity was assessed by tolerance and variance inflation factor, and the role of individual conditioning factors was estimated using information gain ratio. The result reveals that there is no severe multicollinearity among the landslide conditioning factors, and the triggering factor rainfall appeared as the leading cause of the landslide. Based on the final prediction values of each model, LSM was constructed and successfully portioned into five distinct classes, like very low, low, moderate, high, and very high susceptibility. The susceptibility class-wise distribution of landslides shows that more than 90% of the landslide area falls under higher landslide susceptibility grades. The precision of models was examined using the area under the curve (AUC) of the receiver operating characteristics (ROC) curve and statistical methods like root mean square error (RMSE) and mean absolute error (MAE). In both datasets (training and validation), the CNN model achieved the maximum AUC value of 0.903 and 0.939, respectively. The lowest value of RMSE and MAE also reveals the better performance of the CNN model. So, it can be concluded that all the models have performed well, but the CNN model has outperformed the other models in terms of precision.

滑坡被认为是世界丘陵地区对人类生命和财产的最严重威胁之一。随着时间的推移，全球范围内的滑坡数量和破坏程度一直在增加。因此，滑坡管理对于维持丘陵地区的自然和社会经济动态至关重要。Rorachu流域是本研究中锡金最易发生滑坡的地区之一。该研究的主要目标是使用基于计算机的高级机器学习技术来准备滑坡敏感性图（LSM），并比较模型的性能。为了适当地了解滑坡与现有空间的关系，选择了二十个因素，包括触发因素和致病因素。深度学习算法即。卷积神经网络模型（CNN）和三种流行的机器学习技术，即随机森林模型（RF），人工神经网络模型（ANN）和装袋模型，被用于准备LSM。通过随机获取的滑坡点和非滑坡点设计了两个单独的数据集，包括训练和验证。在选择训练点和验证点时，考虑使用70:30的比率。用公差和方差膨胀因子评估多重共线性，并使用信息增益比评估各个条件因子的作用。结果表明，滑坡条件因素之间不存在严重的多重共线性，触发因素降雨是导致滑坡发生的主要原因。根据每个模型的最终预测值，LSM被构建并成功地分为五个不同的类别，如极低，低，中，高和极高的磁化率。滑坡敏感性等级分​​布表明，超过90％的滑坡面积属于较高的滑坡敏感性等级。使用接收器工作特性（ROC）曲线的曲线下面积（AUC）和统计方法，例如均方根误差（RMSE）和平均绝对误差（MAE），检查了模型的精度。在两个数据集中（训练和验证），CNN模型的最大AUC值分别为0.903和0.939。RMSE和MAE的最小值也显示了CNN模型的更好性能。因此，可以得出结论，所有模型均表现良好，