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Stacking ensemble of deep learning methods for landslide susceptibility mapping in the Three Gorges Reservoir area, China

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Abstract

A hybrid framework by integrating stacking ensemble with two deep learning methods of convolutional neural network (CNN) and recurrent neural network (RNN) is introduced in this paper for landslide spatial prediction in the Three Gorges Reservoir area, China. The proposed framework is summarized in following steps. First, a spatial database consists of 20 landslide conditioning factors and 196 landslide polygons was established. Then, landslide and non-landslide pixels were randomly divided into training (70% of the total) and test (30%) sets. Next, a stacking ensemble method that integrates CNN and RNN was constructed using the training set. Finally, the proposed stacking framework was applied for landslide susceptibility mapping and evaluated. Experimental results demonstrated that the proposed framework can obtain the best predictive capability (0.918) than CNN (0.904), RNN (0.900) and logistic regression (0.877) in terms of area under the receiver operating characteristic curve (AUC). Therefore, it can be useful for landslide disaster management and assessment.

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Acknowledgements

The authors are grateful to the Headquarters of Prevention and Control of Geo-Hazards in Area of Three Gorges Reservoir for providing data and material, and would also like to thank the handling editors and four anonymous reviewers for their valuable comments and suggestions, which significantly improved the quality of this paper.

Funding

This work was supported by the Medium and Long Term Development Plan for China's Civil Space Infrastructure (300018000000190078), the National Natural Science Foundation of China (61271408), and the Open Fund of Hubei Key Laboratory of Intelligent Robot (Wuhan Institute of Technology) (HBIR 202002).

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  1. China Institute of Geo-Environment Monitoring, Beijing, 100081, China

    Wenjuan Li

  2. Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan, 430074, China

    Zhice Fang & Yi Wang

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  1. Wenjuan Li
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  2. Zhice Fang
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Contributions

WL: Data curation, Methodology, Validation, Visualization, Roles/Writing—original draft. ZF: Conceptualization, Formal analysis, Supervision, Writing—review & editing. YW: Writing—review & editing, Funding acquisition.

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Correspondence to Zhice Fang.

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Li, W., Fang, Z. & Wang, Y. Stacking ensemble of deep learning methods for landslide susceptibility mapping in the Three Gorges Reservoir area, China. Stoch Environ Res Risk Assess 36, 2207–2228 (2022). https://doi.org/10.1007/s00477-021-02032-x

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