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Landslide susceptibility assessment at the Wuning area, China: a comparison between multi-criteria decision making, bivariate statistical and machine learning methods

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Abstract

The aim of this research is to investigate multi-criteria decision making [spatial multi-criteria evaluation (SMCE)], bivariate statistical methods [frequency ratio (FR), index of entropy (IOE), weighted linear combination (WLC)] and machine learning [support vector machine (SVM)] models for estimating landslide susceptibility at the Wuning area, China. A total of 445 landslides were randomly classified into 70% (311 landslides) and 30% (134 landslides) to train and validate landslide models, respectively. Fourteen landslide conditioning factors including slope angle, slope aspect, altitude, topographic wetness index, stream power index, sediment transport index, soil, lithology, NDVI, land use, rainfall, distance to road, distance to river and distance to fault were then studied for landslide susceptibility assessment. Performances of five studied models were evaluated using area under the ROC curve (AUROC) for training (success rate curve) and validation (prediction rate curve) datasets, statistical-based measures and tests. Results indicated that the area under the success rate curve for the FR, IOE, WLC, SVM and SMCE models was 88.32%, 82.58%, 78.91%, 85.47% and 89.96%, respectively, demonstrating that SMCE could provide the higher accuracy. The prediction capability findings revealed that the SMCE model (AUC = 86.81%) was also the highest approach among the five studied models, followed by the FR (AUC = 84.53%), the SVM (AUC = 81.24%), the IOE (AUC = 79.67%) and WLC (73.92%) methods. The landslide susceptibility maps derived from the above five models are reasonably accurate and could be used to perform elementary land use planning for hazard extenuation.

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Acknowledgements

The authors would like to thank editor and anonymous reviewers for their meaningful comments on the primary version of the manuscript. This research was supported by the Universiti Teknologi Malaysia (UTM) based on Research University Grant (Q.J130000.2527.17H84).

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Authors and Affiliations

  1. Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Nanjing, 210023, People’s Republic of China

    Haoyuan Hong

  2. State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing, 210023, People’s Republic of China

    Haoyuan Hong

  3. Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing, 210023, Jiangsu, People’s Republic of China

    Haoyuan Hong

  4. Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran

    Himan Shahabi

  5. Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran

    Ataollah Shirzadi & Kamran Chapi

  6. College of Geology and Environment, Xi’an University of Science and Technology, Xi’an, 710054, Shaanxi, People’s Republic of China

    Wei Chen

  7. Department of Geoinformation, Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia

    Baharin Bin Ahmad

  8. Discipline of Geography and Spatial Sciences, School of Land and Food, University of Tasmania, Churchill Ave, Hobart, TAS, 7005, Australia

    Majid Shadman Roodposhti

  9. Department of Geography, University of Mohaghegh Ardabili, Ardabil, Iran

    Arastoo Yari Hesar

  10. Jiangxi Provincial Meteorological Observatory, Jiangxi Meteorological Bureau, No. 109 ShengfuBeier Road, Nanchang, 330046, People’s Republic of China

    Yingying Tian

  11. Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Dieu Tien Bui

  12. Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Dieu Tien Bui

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  1. Haoyuan Hong
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Hong, H., Shahabi, H., Shirzadi, A. et al. Landslide susceptibility assessment at the Wuning area, China: a comparison between multi-criteria decision making, bivariate statistical and machine learning methods. Nat Hazards 96, 173–212 (2019). https://doi.org/10.1007/s11069-018-3536-0

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