This paper aims to compare and develop the influence on different sample sizes and sample ratios when using machine learning (ML) models, i.e., support vector machine (SVM) and artificial neural network (ANN), to produce landslide susceptibility maps (LSMs) in Penang Island, Malaysia. At the same time, traditional statistical (TS) models are also considered to produce LSMs in this comparative research. The receiver operating characteristic (ROC) curve and recall metric are applied to evaluate the model’s performance. Based on the evaluation criteria, the ML model outperforms the TS models and the ML models trained using the datasets with larger sample size give a better performance. ML models, especially SVM models, have better performance when training with balanced datasets as well as the datasets of more landslide sample data. Kruskal-Wallis test and Mann-Whitney U test are applied to test the significance. The results indicate that sample size and sample ratio are essential factors when considering ML models to produce LSMs. The LSMs produced in this research can provide valid and useful information to the local authorities for landslide mitigation and prediction.

本文旨在比较和开发在使用支持向量机（SVM）和人工神经网络（ANN）的机器学习（ML）模型来生成滑坡敏感性图（LSM）时，对不同样本量和样本比率的影响。马来西亚槟城岛。同时，在这项比较研究中，传统统计（TS）模型也被认为可以产生LSM。接收器工作特性（ROC）曲线和召回率指标可用于评估模型的性能。基于评估标准，ML模型优于TS模型，并且使用样本量更大的数据集训练的ML模型具有更好的性能。当使用平衡数据集以及更多滑坡样本数据的数据集进行训练时，ML模型（尤其是SVM模型）具有更好的性能。U检验用于检验显着性。结果表明，在考虑使用ML模型生成LSM时，样本大小和样本比率是必不可少的因素。这项研究产生的LSM可以为地方政府减轻和预测滑坡提供有效和有用的信息。