Landslides impact on human activities and socio-economic development, especially in mountainous areas. This study focuses on the comparison of the prediction capability of advanced machine learning techniques for the rainfall-induced shallow landslide susceptibility of Deokjeokri catchment and Karisanri catchment in South Korea. The influencing factors for landslides, i.e., topographic, hydrologic, soil, forest, and geologic factors, are prepared from various sources based on availability, and a multicollinearity test is also performed to select relevant causative factors. The landslide inventory maps of both catchments are obtained from historical information, aerial photographs and performed field surveys. In this study, Deokjeokri catchment is considered as a training area and Karisanri catchment as a testing area. The landslide inventories contain 748 landslide points in training and 219 points in testing areas. Three landslide susceptibility maps using machine learning models, i.e., Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN), are prepared and compared. The outcomes of the analyses are validated using the landslide inventory data. A receiver operating characteristic curve (ROC) method is used to verify the results of the models. The results of this study show that the training accuracy of RF is 0.756 and the testing accuracy is 0.703. Similarly, the training accuracy of XGBoost is 0.757 and testing accuracy is 0.74. The prediction of DNN revealed acceptable agreement between the susceptibility map and the existing landslides, with a training accuracy of 0.855 and testing accuracy of 0.802. The results showed that the DNN model achieved lower prediction error and higher accuracy results than other models for shallow landslide modeling in the study area.

中文翻译：

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使用先进的机器学习算法在两个相邻流域的降雨诱发的浅层滑坡敏感性分析

滑坡影响人类活动和社会经济发展，特别是在山区。这项研究的重点是比较先进的机器学习技术对韩国Deokjeokri流域和Karisanri流域降雨引起的浅层滑坡敏感性的预测能力的比较。滑坡的影响因素（即地形，水文，土壤，森林和地质因素）是根据可用性从各种来源准备的，并且还进行了多重共线性测试以选择相关的成因。这两个流域的滑坡清单图都是从历史信息，航拍照片和实地调查中获得的。在本研究中，Deokjeokri流域被视为培训区域，而Karisanri流域被视为测试区域。滑坡清单中包含748个滑坡点和219个滑坡点。准备并比较了使用机器学习模型的三个滑坡敏感性图，即随机森林（RF），极端梯度增强（XGBoost）和深层神经网络（DNN）。使用滑坡清单数据验证分析结果。接收机工作特性曲线（ROC）方法用于验证模型的结果。研究结果表明，RF的训练精度为0.756，测试精度为0.703。同样，XGBoost的训练精度为0.757，测试精度为0.74。DNN的预测揭示了磁化率图与现有滑坡之间可接受的一致性，训练精度为0.855，测试精度为0.802。