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Landslide Susceptibility Mapping with Deep Learning Algorithms

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  • Jules Maurice Habumugisha

    (Key Laboratory for Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (CAS), Chengdu 610041, China
    University of Chinese Academy of Sciences, Beijing 100049, China
    These authors contributed equally to this work and should be considered as co-first authors.)

  • Ningsheng Chen

    (Key Laboratory for Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (CAS), Chengdu 610041, China
    Academy of Plateau Science and Sustainability, Xining 810016, China)

  • Mahfuzur Rahman

    (Department of Civil Engineering, International University of Business Agriculture and Technology (IUBAT), Dhaka 1230, Bangladesh
    These authors contributed equally to this work and should be considered as co-first authors.)

  • Md Monirul Islam

    (Department of Civil Engineering, International University of Business Agriculture and Technology (IUBAT), Dhaka 1230, Bangladesh)

  • Hilal Ahmad

    (School of Civil and Resource Engineering, University of Science and Technology, Beijing 100083, China)

  • Ahmed Elbeltagi

    (Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura 35516, Egypt
    College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China)

  • Gitika Sharma

    (Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala 147004, Punjab, India)

  • Sharmina Naznin Liza

    (Department of Civil Engineering, Dhaka University of Engineering & Technology (DUET), Gazipur 1707, Bangladesh)

  • Ashraf Dewan

    (School of Earth and Planetary Sciences, Curtin University, Bentley, WA 6102, Australia)

Abstract

Among natural hazards, landslides are devastating in China. However, little is known regarding potential landslide-prone areas in Maoxian County. The goal of this study was to apply four deep learning algorithms, the convolutional neural network (CNN), deep neural network (DNN), long short-term memory (LSTM) networks, and recurrent neural network (RNN) in evaluating the possibility of landslides throughout Maoxian County, Sichuan, China. A total of 1290 landslide records was developed using historical records, field observations, and remote sensing techniques. The landslide susceptibility maps showed that most susceptible areas were along the Minjiang River and in some parts of the southeastern portion of the study area. Slope, rainfall, and distance to faults were the most influential factors affecting landslide occurrence. Results revealed that proportion of landslide susceptible areas in Maoxian County was as follows: identified landslides (13.65–23.71%) and non-landslides (76.29–86.35%). The resultant maps were tested against known landslide locations using the area under the curve (AUC). This study indicated that the DNN algorithm performed better than LSTM, CNN, and RNN in identifying landslides in Maoxian County, with AUC values (for prediction accuracy) of 87.30%, 86.50%, 85.60%, and 82.90%, respectively. The results of this study are useful for future landslide risk reduction along with devising sustainable land use planning in the study area.

Suggested Citation

  • Jules Maurice Habumugisha & Ningsheng Chen & Mahfuzur Rahman & Md Monirul Islam & Hilal Ahmad & Ahmed Elbeltagi & Gitika Sharma & Sharmina Naznin Liza & Ashraf Dewan, 2022. "Landslide Susceptibility Mapping with Deep Learning Algorithms," Sustainability, MDPI, vol. 14(3), pages 1-22, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:3:p:1734-:d:741025
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    Citations

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    Cited by:

    1. Jae-Hyeon Park & Seong-Gyun Park & Hyun Kim, 2022. "Applicability Evaluation of Landslide Vulnerability Criteria for Decision on Landcreep-Vulnerable Areas in South Korea," Sustainability, MDPI, vol. 14(8), pages 1-16, April.
    2. Esteban Bravo-López & Tomás Fernández Del Castillo & Chester Sellers & Jorge Delgado-García, 2023. "Analysis of Conditioning Factors in Cuenca, Ecuador, for Landslide Susceptibility Maps Generation Employing Machine Learning Methods," Land, MDPI, vol. 12(6), pages 1-28, May.
    3. Aihua Wei & Kaining Yu & Fenggang Dai & Fuji Gu & Wanxi Zhang & Yu Liu, 2022. "Application of Tree-Based Ensemble Models to Landslide Susceptibility Mapping: A Comparative Study," Sustainability, MDPI, vol. 14(10), pages 1-15, May.
    4. Cuiying Zhou & Jinwu Ouyang & Zhen Liu & Lihai Zhang, 2022. "Early Risk Warning of Highway Soft Rock Slope Group Using Fuzzy-Based Machine Learning," Sustainability, MDPI, vol. 14(6), pages 1-28, March.
    5. Han Zhang & Chao Yin & Shaoping Wang & Bing Guo, 2023. "Landslide susceptibility mapping based on landslide classification and improved convolutional neural networks," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(2), pages 1931-1971, March.
    6. Hubert Szczepaniuk & Edyta Karolina Szczepaniuk, 2022. "Applications of Artificial Intelligence Algorithms in the Energy Sector," Energies, MDPI, vol. 16(1), pages 1-24, December.
    7. Lingfan Ju & Huan Yu & Qing Xiang & Wenkai Hu & Xiaoyu Xu, 2023. "Spatial Coupling Pattern and Driving Forces of Rural Settlements and Arable Land in Alpine Canyon Region of the Maoxian County, China," IJERPH, MDPI, vol. 20(5), pages 1-18, February.
    8. Mohammad Amin Khalili & Luigi Guerriero & Mostafa Pouralizadeh & Domenico Calcaterra & Diego Martire, 2023. "Monitoring and prediction of landslide-related deformation based on the GCN-LSTM algorithm and SAR imagery," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 119(1), pages 39-68, October.

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