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Landslide susceptibility assessment in the Anfu County, China: comparing different statistical and probabilistic models considering the new topo-hydrological factor (HAND)

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Abstract

The present study is aimed at producing landslide susceptibility map of a landslide-prone area (Anfu County, China) by using evidential belief function (EBF), frequency ratio (FR) and Mahalanobis distance (MD) models. To this aim, 302 landslides were mapped based on earlier reports and aerial photographs, as well as, carrying out several field surveys. The landslide inventory was randomly split into a training dataset (70%; 212landslides) for training the models and the remaining (30%; 90 landslides) was cast off for validation purpose. A total of sixteen geo-environmental conditioning factors were considered as inputs to the models: slope degree, slope aspect, plan curvature, profile curvature, the new topo-hydrological factor termed height above the nearest drainage (HAND), average annual rainfall, altitude, distance from rivers, distance from roads, distance from faults, lithology, normalized difference vegetation index (NDVI), sediment transport index (STI), stream power index (SPI), soil texture, and land use/cover. The validation of susceptibility maps was evaluated using the area under the receiver operating characteristic curve (AUROC). As a results, the FR outperformed other models with an AUROC of 84.98%, followed by EBF (78.63%) and MD (78.50%) models. The percentage of susceptibility classes for each model revealed that MD model managed to build a compendious map focused at highly susceptible areas (high and very high classes) with an overall area of approximately 17%, followed by FR (22.76%) and EBF (31%). The premier model (FR) attested that the five factors mostly influenced the landslide occurrence in the area: NDVI, soil texture, slope degree, altitude, and HAND. Interestingly, HAND could manifest clearer pattern with regard to landslide occurrence compared to other topo-hydrological factors such as SPI, STI, and distance to rivers. Lastly, it can be conceived that the susceptibility of the area to landsliding is more subjected to a complex environmental set of factors rather than anthropological ones (residential areas and distance to roads). This upshot can make a platform for further pragmatic measures regarding hazard-planning actions.

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Acknowledgements

The authors would like to acknowledge the anonymous reviewers and the editor for their helpful comments on a previous version of the manuscript. Also, the authors wish to express their sincere thanks to Universiti Teknologi Malaysia (UTM) based on Research University Grant (Q.J130000.2527.17H84) for their financial supports in this research.

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Authors and Affiliations

  1. Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing, 210023, China

    Haoyuan Hong, Junzhi Liu & A-Xing Zhu

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

    Haoyuan Hong, Junzhi Liu & A-Xing Zhu

  3. Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing, 210023, Jiangsu, China

    Haoyuan Hong, Junzhi Liu & A-Xing Zhu

  4. Young Researchers and Elite Club, Gorgan Branch, Islamic Azad University, Gorgan, Iran

    Aiding Kornejady

  5. Faculty member of the Department of Agricultural Technology & Engineering, Payam Noor University, Tehran, Iran

    Adel Soltani

  6. Faculty of Geodesy & Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran

    Seyed Vahid Razavi Termeh

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

    Arastoo Yari hesar

  8. Department of Geoinformation, Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia (UTM), Skudai, Malaysia

    Baharin Bin Ahmad

  9. Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan, 430074, China

    Yi Wang

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  1. Haoyuan Hong
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Correspondence to A-Xing Zhu or Yi Wang.

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Communicated by: H. A. Babaie

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Hong, H., Kornejady, A., Soltani, A. et al. Landslide susceptibility assessment in the Anfu County, China: comparing different statistical and probabilistic models considering the new topo-hydrological factor (HAND). Earth Sci Inform 11, 605–622 (2018). https://doi.org/10.1007/s12145-018-0352-8

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