: Landslides are known as the world’s most dangerous threat in mountainous regions and pose a critical obstacle for both economic and infrastructural progress. It is, therefore, quite relevant to discuss the pattern of spatial incidence of this phenomenon. The current research manifests a set of individual and ensemble of machine learning and probabilistic approaches like an artificial neural network (ANN), support vector machine (SVM), random forest (RF), logistic regression (LR), and their ensembles such as ANN-RF, ANN-SVM, SVM-RF, SVM-LR, LR-RF, LR-ANN, ANN-LR-RF, ANN-RF-SVM, ANN-SVM-LR, RF-SVM-LR, and ANN-RF-SVM-LR for mapping landslide susceptibility in Rudraprayag district of Garhwal Himalaya, India. A landslide inventory map along with sixteen landslide conditioning factors (LCFs) was used. Randomly partitioned sets of 70%:30% were used to ascertain the goodness of fit and predictive ability of the models. The contribution of LCFs was analyzed using the RF model. The altitude and drainage density were found to be the responsible factors in causing the landslide in the study area according to the RF model. The robustness of models was assessed through three threshold dependent measures, i.e., receiver operating characteristic (ROC), precision and accuracy, and two threshold independent measures, i.e., mean-absolute-error (MAE) and root-mean-square-error (RMSE). Finally, using the compound factor (CF) method, the models were prioritized based on the results of the validation methods to choose best model. Results show that ANN-RF-LR indicated a realistic finding, concentrating only on 17.74% of the study area as highly susceptible to landslide. The ANN-RF-LR ensemble demonstrated the highest goodness of fit and predictive capacity with respective values of 87.83% (area under the success rate curve) and 93.98% (area under prediction rate curve), and the highest robustness correspondingly. These attempts will play a significant role in ensemble modeling, in building reliable and comprehensive models. The proposed ANN-RF-LR ensemble model may be used in the other geographic areas having similar geo-environmental conditions. It may also be used in other types of geo-hazard modeling.

中文翻译：

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在Garhwal喜马拉雅山Rudraprayag区评估单个和新型机器学习集成的性能以及滑坡敏感性评估的统计模型

： 滑坡是世界上最危险的山区威胁，对经济和基础设施的发展都构​​成了严重的障碍。因此，讨论这种现象的空间发生模式非常重要。当前的研究表明了一组单独的机器学习和概率方法集合，例如人工神经网络（ANN），支持向量机（SVM），随机森林（RF），逻辑回归（LR）以及它们的集合，例如ANN -RF，ANN-SVM，SVM-RF，SVM-LR，LR-RF，LR-ANN，ANN-LR-RF，ANN-RF-SVM，ANN-SVM-LR，RF-SVM-LR和ANN- RF-SVM-LR用于绘制印度Garhwal喜马拉雅山Rudraprayag区的滑坡敏感性图。使用了滑坡清单图以及16个滑坡条件因子（LCF）。70％的随机分区集：30％用于确定模型的拟合优度和预测能力。使用RF模型分析了LCF的贡献。根据RF模型，海拔高度和排水密度是导致研究区域发生滑坡的主要因素。模型的鲁棒性通过三个阈值相关度量（即接收器工作特性（ROC），精度和准确性）和两个阈值独立度量（即均值绝对误差（MAE）和均方根误差（ RMSE）。最后，使用复合因子（CF）方法，根据验证方法的结果对模型进行优先排序，以选择最佳模型。结果表明，ANN-RF-LR表明是一个现实的发现，仅集中在研究区域的17.74％高度易受滑坡影响。ANN-RF-LR集合表现出最高的拟合度和预测能力，分别为87.83％（成功率曲线下的面积）和93.98％（预测率曲线下的面积），并且相应地具有最高的鲁棒性。这些尝试将在集成建模，构建可靠而全面的模型中发挥重要作用。所提出的ANN-RF-LR集成模型可用于具有相似地理环境条件的其他地理区域。它也可以用于其他类型的地质灾害建模中。建立可靠和全面的模型。所提出的ANN-RF-LR集成模型可用于具有相似地理环境条件的其他地理区域。它也可以用于其他类型的地质灾害建模中。建立可靠和全面的模型。所提出的ANN-RF-LR集成模型可用于具有相似地理环境条件的其他地理区域。它也可以用于其他类型的地质灾害建模中。