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Landslide Susceptibility Mapping Based on the Deep Belief Network: A Case Study in Sichuan Province, China

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Understanding and Reducing Landslide Disaster Risk (WLF 2020)

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Abstract

Landslides dataset of Sichuan Province in China, containing 1551 historical individual landslides, is a result of two teams’ effort in the past few years, based on which landslide susceptibility can be mapped. Considering complex internal relations among the triggering factors, logistic regression (LR) and shallow neural networks, such as back propagation neural network (BPNN), are often limited. In this paper, we make a straightforward development that the deep belief network (DBN) based on deep learning technology is introduced to map the regional landslide susceptibility. Seven key factors with respect to geomorphology, geology, and hydrology are considered, and a DBN model containing three pre-trained layers of Restricted Boltzmann Machines (RBM) by stochastic gradient descent (SGD) method is configured to obtain the landslide susceptibility. In the receive operator characteristic (ROC) analysis, comparing DBN with LR and BPNN shows that DBN has a better prediction precision, with lower false alarm rate and fake alarm rate. This research will contribute to a better-performance model for regional-scale landslide susceptibility mapping, in particular at the area where triggering factors show complex relation and relative independence.

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Acknowledgements

This study was financially supported by the National Natural Science Foundation of China (Grant No. 51478483, W. Wang; Grant No. 41702310, Z. Han); the National Key R&D Program of China (Grant No. 2018YFC1505401, Z. Han); and the Natural Science Foundation of Hunan (Grant No. 2018JJ3644, Z. Han). These financial supports are gratefully acknowledged.

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Authors and Affiliations

  1. School of Civil Engineering, Central South University, Shaoshan South Road 22, Changsha, 410075, China

    Wei-Dong Wang, Zhuolei He, Zheng Han & Yange Li

Authors
  1. Wei-Dong Wang
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  2. Zhuolei He
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  3. Zheng Han
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  4. Yange Li
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Corresponding author

Correspondence to Zheng Han .

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Editors and Affiliations

  1. Department of Civil Protection, Rome, Italy

    Fausto Guzzetti

  2. Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb, Zagreb, Croatia

    Snježana Mihalić Arbanas

  3. Research institute for Geo-Hydrological Protection (Italian National Research Council), Perugia, Italy

    Paola Reichenbach

  4. International Consortium on Landslides, Kyoto, Japan

    Kyoji Sassa

  5. Geological Survey of Canada, Sidney, BC, Canada

    Peter T. Bobrowsky

  6. Graduate School of Advanced Integrated Studies in Human Survivability (Shishu-kan), Kyoto University, Kyoto, Japan

    Kaoru Takara

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Wang, WD., He, Z., Han, Z., Li, Y. (2021). Landslide Susceptibility Mapping Based on the Deep Belief Network: A Case Study in Sichuan Province, China. In: Guzzetti, F., Mihalić Arbanas, S., Reichenbach, P., 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/10.1007/978-3-030-60227-7_22

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