Abstract

Landslides are a form of soil erosion threatening the sustainability of some areas of the world. There is, therefore, a need to investigate landslide rates and behaviour. In this research, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a meta classifier based on reduced error pruning tree (REPTree) as a base classifier called RF-REPTree, for landslide susceptibility mapping (LSM) in the Kalaleh watershed, Golestan Province, Iran. Some benchmark models, including the open-source Java decision tree (J48), naive Bayes tree (NBTree), and REPTree were used to compare the designed model. A total of 249 landslide locations were identified and mapped. The group was split into training (70%) and testing (30%) data for modelling and reliability analysis. Based on a literature review and multi-collinearity tests, 16 landslide conditioning factors (LCFs) were selected. Of the LCFs, the topographical position index (TPI) had the highest correlation with landslide occurrence. The LSM produced by RF-REPTree revealed that nearly 29% of the study areas have high to very high landslide susceptibility (LS). Statistical analysis of the model results included the receiver operating characteristic curve (ROC), the efficiency test, the true skill statistic (TSS), and the kappa index. ROC demonstrated that the AUC values of RF-REPTree, REPTree, J48, and NBTree models were 0.832, 0.700, 0.695, and 0.759 for succession rate curves and 0.794, 0.740, 0.788, and 0.728 for prediction rate curves, respectively. Therefore, all models were judged to be acceptably accurate for LSM. Among the LS models, the RF-REPTree model achieved the highest accuracy, followed by REPTree, J48, and NBTree. The results of LSM can be used to target the mitigation of landslide hazards and provide a foundation for sustainable environmental planning.

摘要

滑坡是威胁世界某些地区可持续性的土壤侵蚀的一种形式。因此，有必要调查滑坡速率和行为。在这项研究中，我们引入了一种新的混合人工智能方法，将旋转森林 (RF) 作为元分类器，基于减少误差修剪树 (REPTree) 作为基础分类器，称为 RF-REPTree，用于 Kalaleh 的滑坡敏感性映射 (LSM)伊朗戈勒斯坦省的分水岭。一些基准模型，包括开源 Java 决策树 (J48)、朴素贝叶斯树 (NBTree) 和 REPTree 用于比较设计的模型。总共确定并绘制了 249 个滑坡位置。该组分为训练 (70%) 和测试 (30%) 数据，用于建模和可靠性分析。基于文献综述和多重共线性检验，选择了 16 个滑坡条件因子 (LCF)。在 LCF 中，地形位置指数 (TPI) 与滑坡发生的相关性最高。RF-REPTree 生成的 LSM 显示，近 29% 的研究区域具有高到非常高的滑坡敏感性 (LS)。模型结果的统计分析包括受试者工作特征曲线 (ROC)、效率检验、真实技能统计 (TSS) 和 kappa 指数。ROC 证明，RF-REPTree、REPTree、J48 和 NBTree 模型的 AUC 值对于继承率曲线分别为 0.832、0.700、0.695 和 0.759，对于预测率曲线分别为 0.794、0.740、0.788 和 0.728。因此，所有模型都被判断为 LSM 的准确度可以接受。在 LS 模型中，RF-REPTree 模型的准确率最高，其次是 REPTree、J48、和NBTree。LSM 的结果可用于减轻滑坡危害，并为可持续环境规划提供基础。