Abstract
BackgroundLandslide-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.ResultsThe 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%).ConclusionsThis 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.
Highlights
Landslide susceptibility assessment is a cogent research topic intended to determine the spatial probability of landslide occurrence since landslides continuously result in damages and casualties worldwide (Corominas et al 2013)
The quantitative methods most widely used for landslide susceptibility mapping are such as logistic regression (Lee and Talib 2005; Ayalew and Yamagishi 2005; Bai et al 2010; Aditian et al 2018), naïve Bayes (Tien Bui et al 2012; Tsangaratos and Ilia 2016), artificial neural networks (Pradhan et al 2010; Arnone et al 2016), support vector machines (Yao et al 2008; Yilmaz 2010; Ballabio and Sterlacchini 2012; Xu et al 2012), decision trees (Saito et al 2009; Yeon et al 2010), and random forest (Alessandro et al 2015; Trigila et al 2015; Hong et al 2016; Chen et al 2019b; Park et al 2019) in machine learning techniques
This study was performed using the following main steps (Fig. 6): (1) correlation analysis between landslide inventory and landslide-affecting factors using frequency ratio, (2) landslide susceptibility prediction using support vector machine (SVM) and random forest (RF) models in machine learning, (3) landslide susceptibility prediction using stacked autoencoder (StAE) and sparse autoencoder (SpAE) employing back propagation neural network in deep learning, (4) evaluation of StAE and SpAE combined with machine learning acquired from a better prediction ratio between SVM and RF, and (5) validation and comparison of predictive performance from the area under the curves and landslide susceptibility maps produced by six models
Summary
Landslide susceptibility assessment is a cogent research topic intended to determine the spatial probability of landslide occurrence since landslides continuously result in damages and casualties worldwide (Corominas et al 2013). Deep learning-based autoencoder is a semi-unsupervised learning method with no prior knowledge, such as landslide inventory, which means that landslide and non-landslide labels and linear and non-linear correlation assumptions are not needed (Huang et al 2019). Traditional methods for de-correlation are based on the prior assumption that there are linear correlations between landslides and non-landslides. The autoencoder driven by data rather than prior knowledge can transform raw data into non-linear correlated features. Landslide-affecting factors are uncorrelated or non-linearly correlated, limiting the predictive performance of traditional machine learning methods for landslide susceptibility assessment. Oda City and Gotsu City in Shimane Prefecture, southwestern Japan, were used for susceptibility assessment and prediction of landslides triggered by extreme rainfall
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