ABSTRACT

In this paper, we developed highly accurate ensemble machine learning models integrating Reduced Error Pruning Tree (REPT) as a base classifier with the Bagging (B), Decorate (D), and Random Subspace (RSS) ensemble learning techniques for spatial prediction of rainfall-induced landslides in the Uttarkashi district, located in the Himalayan range, India. To do so, a total of 103 historical landslide events were linked to twelve conditioning factors for generating training and validation datasets. Root Mean Square Error (RMSE) and Area Under the receiver operating characteristic Curve (AUC) were used to evaluate the training and validation performances of the models. The results showed that the single REPT model and its derived ensembles provided a satisfactory accuracy for the prediction of landslides. The D-REPT model with RMSE = 0.351 and AUC = 0.907 was identified as the most accurate model, followed by RSS-REPT (RMSE = 0.353 and AUC = 0.898), B-REPT (RMSE = 0.396 and AUC = 0.876), and the single REPT model (RMSE = 0.398 and AUC = 0.836), respectively. The prominent ensemble models proposed and verified in this study provide engineers and modelers with insights for development of more advanced predictive models for different landslide-susceptible areas around the world.

摘要

在本文中，我们开发了高度准确的集成机器学习模型，该模型将减少错误修剪树（REPT）作为基本分类器与Bagging（B），Decorate（D）和Random Subspace（RSS）集成学习技术相结合，用于降雨的空间预测在印度喜马拉雅山脉的Uttarkashi地区引起的滑坡。为此，将总共103个历史滑坡事件与十二个条件因子相关联，以生成训练和验证数据集。均方根误差（RMSE）和接收器工作特征曲线下面积（AUC）用于评估模型的训练和验证性能。结果表明，单个REPT模型及其派生集合为滑坡的预测提供了令人满意的精度。具有RMSE = 0.351和AUC = 0的D-REPT模型。907被确定为最准确的模型，其次是RSS-REPT（RMSE = 0.353和AUC = 0.898），B-REPT（RMSE = 0.396和AUC = 0.876）和单个REPT模型（RMSE = 0.398和AUC = 0.836） ）， 分别。在这项研究中提出并验证的著名集合模型为工程师和建模者提供了洞察力，以开发针对世界各地不同滑坡易感地区的更高级的预测模型。