Abstract
Regional landslide susceptibility map (LSM) is often taken as a significant reference in emergency response and disaster mitigation. In recent years, convolutional neural network (CNN) has been proven to be effective in extracting complex and high-dimension feature information, alleviating the demand for substantial expertise. In this study, convolutional neural network (CNN-3D) with a spatial-channel attention block embedded was applied to generate the LSM for two earthquake-affected regions (Jiuzhaigou, China, and Iburi, Japan). To explore the capability of the proposed model, standard CNN-2D and random forest (RF) were introduced as benchmarks. Besides, receiver operating characteristic (ROC), F1-score, and Kappa index (Kappa) were chosen to quantitatively compare the performance. The results indicated that the CNN-based model possessed more excellent ability than machine learning-based (ML) model in predicting landslides and the proposed model generated the most accurate and smoother LSM. In particular, CNN-3D obtained the highest F1-score and Kappa (0.93 and 0.85 in Jiuzhaigou, 0.96 and 0.92 in Iburi), demonstrating that the proposed model can effectively exploit 3D information of landslides and address non-linear relationship in high dimension. This study confirmed that CNN-3D can serve as an alternative approach to generate LSM rapidly after an earthquake.
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This study was supported by the National Institute of Natural Hazards, Ministry of Emergency Management of China (ZDJ2021-14) and the Lhasa National Geophysical Observation and Research Station (NORSLS20-07).
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C.X. proposed the research concept and offered the remote sensing data; X.S provided the related controlling factors in the Iburi region; S.M provided the landslide dataset and controlling factors in the Jiuzhaigou region. L.L reformatted the manuscript and participated in the interpretation work in Jiuzhaigou region; Z.Y. designed the framework of the research, conducted the experiment, and wrote the manuscript.
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Yang, Z., Xu, C., Shao, X. et al. Landslide susceptibility mapping based on CNN-3D algorithm with attention module embedded. Bull Eng Geol Environ 81, 412 (2022). https://doi.org/10.1007/s10064-022-02889-4
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DOI: https://doi.org/10.1007/s10064-022-02889-4