Abstract

This study tests the applicability of three resampling methods (i.e. bootstrapping, random-subsampling and cross-validation) for enhancing the performance of eight machine-learning models: boosted regression trees, flexible discriminant analysis, random forests, mixture discriminate analysis, multivariate adaptive regression splines, classification and regression trees, support vector machines and generalized linear models, compared to the use of the original data. The results of models were evaluated using correlation (COR), area under curve (AUC), true skill statistic (TSS), receiver-operating characteristic and the probability of detection (POD). The evaluation showed that the bootstrapping technique improved the performance of all models. The Bootstrapping-random forest (with COR = 0.75, AUC = 0.92, TSS = 0.80 and POD = 0.98) proved to be the best model for landslide prediction. Among the 18 contributing factors, distance from fault, curvature and precipitation were the most influential in all 32 models .

• Highlights

• Hazard prediction of landslide by the 8 machine-learning (ML) models.

• Multiple morphometric, climatic, geologic, vegetation and human factors were used.

• Tests the applicability of three resampling methods.

• The performance of the ML models and coupling models were assessed.

摘要

这项研究测试了三种重采样方法（即自举，随机子采样和交叉验证）对提高八个机器学习模型的性能的适用性：增强回归树，灵活判别分析，随机森林，混合判别分析，多元自适应回归与使用原始数据相比，样条曲线，分类树和回归树，支持向量机和广义线性模型。使用相关性（COR），曲线下面积（AUC），真实技能统计（TSS），接收者操作特征和检测概率（POD）评估模型的结果。评估表明，自举技术提高了所有模型的性能。自举随机森林（COR = 0.75，AUC = 0.92，TSS = 0.80和POD = 0。98）被证明是滑坡预测的最佳模型。在18个影响因素中，距断层，曲率和降水的距离在所有32个模型中影响最大。

• 强调

• 通过8种机器学习（ML）模型预测滑坡的危害。

• 使用了多种形态，气候，地质，植被和人为因素。

• 测试三种重采样方法的适用性。

• 评估了ML模型和耦合模型的性能。