Landslide-affecting factors are uncorrelated or non-linearly correlated, limiting the predictive performance of traditional machine learning methods for landslide susceptibility assessment. Deep learning methods can take advantage of the high-level representation and reconstruction of information from landslide-affecting factors. In this paper, a novel deep learning-based algorithm that combine classifiers of both deep learning and machine learning is proposed for landslide susceptibility assessment. A stacked autoencoder (StAE) and a sparse autoencoder (SpAE) both consist of an input layer for raw data, hidden layer for feature extraction, and output layer for classification and prediction. As a study case, Oda City and Gotsu City in Shimane Prefecture, southwestern Japan, were used for susceptibility assessment and prediction of landslides triggered by extreme rainfall. The prediction performance was compared by analyzing real landslide and non-landslide data. The prediction performance of random forest (RF) was evaluated as better than that of a support vector machine (SVM) in traditional machine learning, so RF was combined with both StAE and SpAE. The results show that the prediction ratio of the combined classifiers was 93.2% for StAE combined with RF model and 92.5% for SpAE combined with RF model, which were higher than those of the SVM (87.4%), RF (89.7%), StAE (84.2%), and SpAE (88.2%). This study provides an example of combined classifiers giving a better predictive ratio than a single classifier. The asymmetric and unsupervised autoencoder combined with RF can exploit optimal non-linear features from landslide-affecting factors successfully, outperforms some conventional machine learning methods, and is promising for landslide susceptibility assessment.

中文翻译：

---

使用自动编码器结合随机森林在日本岛根县进行的极端降雨诱发的滑坡敏感性评估

滑坡影响因素不相关或非线性相关，从而限制了传统机器学习方法对滑坡敏感性评估的预测性能。深度学习方法可以利用高水平表示和从滑坡影响因素中重建信息的优势。本文提出了一种新的基于深度学习的算法，该算法结合了深度学习和机器学习的分类器，用于滑坡敏感性评估。堆叠式自动编码器（StAE）和稀疏自动编码器（SpAE）都由用于原始数据的输入层，用于特征提取的隐藏层以及用于分类和预测的输出层组成。例如，日本西南部岛根县的尾田市和五津市，被用于敏感性评估和预测由极端降雨引发的滑坡。通过分析实际滑坡和非滑坡数据比较了预测性能。在传统机器学习中，随机森林（RF）的预测性能优于支持向量机（SVM）的预测性能，因此将RF与StAE和SpAE结合使用。结果表明，StAE结合RF模型的组合分类器的预测率为93.2％，SpAE结合RF模型的组合分类器的预测率为92.5％，高于SVM（87.4％），RF（89.7％），StAE （84.2％）和SpAE（88.2％）。这项研究提供了一个组合分类器的示例，该示例提供了比单个分类器更好的预测率。