Abstract

In this study, we have developed five spatially explicit ensemble predictive machine learning models for the landslide susceptibility mapping of the Van Chan district of the Yen Bai Province, Vietnam. In the model studies, Random Subspace (RSS) was used as the ensemble learner with Best First Decision Tree (BFT), Functional Tree (FT), J48 Decision Tree (J48DT), Naïve Bayes Tree (NBT) and Reduced Error Pruning Trees (REPT) as the base classifiers. Data of 167 past and present landslides and various landslide conditioning factors were used for generation of the datasets. The results showed that the RSSFT model achieved the highest performance in terms of Fgiurepredicting future landslides, followed by RSSREPT, RSSBFT, RSSJ48, and RSSNBT, respectively. Therefore, the RSSFT model was found to be more robust model than the other studied models, which can be used in other areas of landslide susceptibility mapping for proper landuse planning and management.

摘要

在这项研究中，我们开发了五个空间显式集成预测机器学习模型，用于越南安白省 Van Chan 区的滑坡敏感性绘图。在模型研究中，随机子空间（RSS）被用作集成学习器，具有最佳第一决策树（BFT）、功能树（FT）、J48决策树（J48DT）、朴素贝叶斯树（NBT）和减少错误修剪树（ REPT）作为基本分类器。167个过去和现在的滑坡数据和各种滑坡条件因素被用于生成数据集。结果表明，RSSFT模型在预测未来滑坡的Fgiure方面取得了最高的性能，其次是RSSREPT、RSSBFT、RSSJ48和RSSNBT。因此，发现 RSSFT 模型比其他研究模型更稳健，