Uttarkashi region is highly prone to landslides because of its geological structure. The exact occurrence of these landslides events is difficult to predict due to its complex mechanism and dependence on the number of triggering factors. Moreover, the behavior and prediction of unstable slopes are of high importance failing of which otherwise can have a devastating impact. This research work aims at studying and modeling slopes with the help of supervised machine learning models: Support vector machine, Backpropagation, Random Forest, and Bayesian Network models. To train and test these models a total of 629 instances are taken. Moreover, the independence of individual features is studied with the help of multicollinearity analysis. The capability of considered methods was evaluated using various performance evaluation metrics. Evaluation and comparison of the results show that the performance of all classifiers is satisfactory for slope failure analysis (AUC=0.595–0.915). Based on the results Random Forest proved to be most efficient to predict slope failures (Accuracy=88%, AUC=0.915). These outcomes can be beneficial for government agencies in early-stage risk mitigation.

中文翻译：

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机器学习技术来预测北阿坎德邦北阿塔卡什（印度）的边坡破坏

由于地理结构，Uttarkashi地区极易发生滑坡。这些滑坡事件的确切发生因其复杂的机制和对触发因素的数量的依赖而难以预测。而且，不稳定斜坡的行为和预测具有高度重要性，否则，如果不这样做会产生毁灭性的影响。这项研究工作旨在借助监督机器学习模型来研究和建模斜坡：支持向量机，反向传播，随机森林和贝叶斯网络模型。为了训练和测试这些模型，总共采用了629个实例。此外，借助多重共线性分析研究了单个特征的独立性。使用各种性能评估指标评估了所考虑方法的能力。结果的评估和比较表明，所有分类器的性能对于边坡破坏分析都是令人满意的（AUC = 0.595–0.915）。根据结果​​，随机森林被证明是预测坡度破坏最有效的方法（准确度= 88％，AUC = 0.915）。这些结果对于政府机构减轻早期风险可能是有益的。