An extreme rainfall-induced landslide susceptibility assessment using autoencoder combined with random forest in Shimane Prefecture, Japan

Background 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 inf...

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Bibliographic Details
Published in:Geoenvironmental disasters Vol. 7; no. 1; pp. 1 - 16
Main Authors: Nam, Kounghoon, Wang, Fawu
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 30.01.2020
Springer Nature B.V
SpringerOpen
Subjects:
Algorithms
Classifiers
Deep learning
Earth and Environmental Science
Earth Sciences
Environment
Environmental Science and Engineering
Extreme weather
Feature extraction
Geoecology/Natural Processes
Geography
Landslide susceptibility
Landslides
Learning algorithms
Machine learning
Modelling of fast landslides
Natural Hazards
Performance prediction
Rain
Rainfall
Random forest
Sparse autoencoder
Stacked autoencoder
Support vector machine
Support vector machines
Japan
Stacked autoencoder
Random forest
Sparse autoencoder
Support vector machine
Landslide susceptibility
ISSN:2197-8670, 2197-8670
Online Access:Get full text
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Summary:Background 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. Results 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%). Conclusions 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.
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ISSN:2197-8670
2197-8670
DOI:10.1186/s40677-020-0143-7