Abstract
The purpose of this study is to evaluate Autoencoder-based landslide susceptibility areas triggered by extreme rainfall in Shimane Prefecture, Japan. 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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Huang Y, Zhao L (2018) Review on landslide susceptibility mapping using support vector machines. CATENA 165:520–529
Huang F, Zhang J, Zhou C, Wang Y, Huang J, Zhu L (2019) A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction. Landslides 17:1–13
Nam KH, Wang FW (2019a) An extreme rainfall-induced landslide susceptibility assessment using autoencoder combined with random forest in Shimane Prefecture. Japan. Geoenvironmental Disasters 7(6):1–16
Nam KH, Wang FW (2019b) The performance of using an autoencoder for prediction and susceptibility assessment of landslides: A case study on landslides triggered by the 2018 Hokkaido Eastern Iburi earthquake in Japan. Geoenvironmental Disasters 6(19):1–14
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Nam, K., Wang, F. (2021). Extreme Rainfall Induced Landslide Susceptibility Assessment Using Autoencoder Combined with Random Forest. In: VilÃmek, V., Wang, F., Strom, A., Sassa, K., Bobrowsky, P.T., Takara, K. (eds) Understanding and Reducing Landslide Disaster Risk. WLF 2020. ICL Contribution to Landslide Disaster Risk Reduction. Springer, Cham. https://doi.org/10.1007/978-3-030-60319-9_36
Download citation
DOI: https://doi.org/10.1007/978-3-030-60319-9_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60318-2
Online ISBN: 978-3-030-60319-9
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)