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Comparison of hybrid data-driven and physical models for landslide susceptibility mapping at regional scales

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Abstract

Landslide susceptibility mapping (LSM) is essential for the spatial prediction of landslides and risk prevention. Physically based LSM models are confined by oversimplifications of physical processes and limited information about soil properties. Data-driven LSM models may give reliable results only when the training and the testing data have high similarity, and application in regions with different geological conditions is often inapplicable. This paper proposes four hybrid data-driven and physical models and compares these models in terms of cross-regional generalization ability and prediction uncertainty. The effects of physical module performance on the hybrid model are analyzed. For the physical modules of the four hybrid models, two-dimensional (2D) physically based models, TRIGRS and the infinite-slope stability models (ISSMs), and the three-dimensional (3D) physically based model, Scoops3D, are adopted. The data-driven modules all adopt the convolutional neural network (CNN) model. Two towns in the Three-Gorge Reservoir area of China are used as the training and testing areas. The results show that all hybrid models have better generalization ability than using the data-driven module exclusively. The prediction uncertainty is significantly reduced by pre-selecting training samples using the physical module. The optimal hybrid model is the one that integrates CNN and ISSM (under the saturated condition). It is then applied to a new region (Wushan County) to further validate the generalization ability. It can make accurate predictions without calibrating the trained model using new data from the validation area. Finally, the effectiveness of model averaging for improving the prediction performance is verified. Using model averaging, the AUC value in the validation area yields 0.834, which is even higher than the original realizations, with AUC values ranging from 0.683 to 0.817. Therefore, the optimal hybrid model can be directly used for LSM in the Three-Gorge Reservoir, and the findings can provide valuable guidance for generalization ability improvement and prediction uncertainty reduction of LSM models in other countries and regions.

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Acknowledgements

The work in this paper was supported by the Natural Science Foundation of China (Project Nos. 52025094, 52088102, 51979158). The authors are grateful for the support from Shanghai Municipal Education Commission (Project No. 2021-01-07-00-02-E00089).

Funding

The work in this paper was supported by the Natural Science Foundation of China (Project Nos. 52025094, 52088102, 51979158). The authors are grateful for the support from Shanghai Municipal Education Commission (Project No. 2021-01-07-00-02-E00089).

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Authors and Affiliations

  1. State Key Laboratory of Ocean Engineering, Department of Civil Engineering, Shanghai Jiao Tong University, Room B522, Mulan Building, 800 Dongchuan Road, Shanghai, 200240, China

    Xin Wei, Lulu Zhang, Yangming Chen & Lin Tan

  2. Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration (CISSE), Shanghai, 200240, China

    Xin Wei, Lulu Zhang, Yangming Chen & Lin Tan

  3. Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, Shanghai, 200240, China

    Xin Wei, Lulu Zhang, Yangming Chen & Lin Tan

  4. Department of Civil and Environmental Engineering, MAE Center: Creating a Multi-Hazard Approach to Engineering, University of Illinois at Urbana-Champaign, 205 N. Mathews Ave, Urbana, IL, 61801, USA

    Xin Wei & Paolo Gardoni

  5. Chongqing Bureau of Geology and Mineral Resources, Chongqing, China

    Dongsheng Liu

  6. Hydrogeology & Engineering Team 208, Chongqing Bureau of Geological Exploration (Chongqing Reconnaissance and Design Academy of Geological Disasters Prevention and Treatment Engineering), Chongqing, China

    Chunlan Du & Hai Li

Authors
  1. Xin Wei
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  2. Lulu Zhang
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  3. Paolo Gardoni
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  4. Yangming Chen
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  5. Lin Tan
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  6. Dongsheng Liu
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  8. Hai Li
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Contributions

XW contributed to conceptualization, methodology, field investigation, data curation, and writing—original draft; LZ contributed to conceptualization, writing—reviewing and editing, supervision, and funding acquisition; PG contributed to writing—reviewing and editing, supervision; YC contributed to writing—reviewing; LT contributed to data curation; DL contributed to field investigation and data support; CD contributed to field investigation and data support; and HL contributed to field investigation and data support.

Corresponding author

Correspondence to Lulu Zhang.

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Wei, X., Zhang, L., Gardoni, P. et al. Comparison of hybrid data-driven and physical models for landslide susceptibility mapping at regional scales. Acta Geotech. 18, 4453–4476 (2023). https://doi.org/10.1007/s11440-023-01841-4

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