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GIS-based landslide spatial modeling in Ganzhou City, China

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Abstract

Landslide susceptibility mapping is among the first works for disaster management and land use planning activities in a mountain area like Ganzhou City. The aims of the current study are to assess GIS-based landslide spatial modeling using four models, namely data-driven evidential belief function (EBF), frequency ratio (FR), maximum entropy (Maxent), and logistic regression (LR), and to compare their performances. At first, a landslide inventory map was prepared according to aerial photographs, satellite images, and extensive field surveys. In total, 3971 landslide events were recognized in the study area that used 2979 landslides (75 %) for modeling and 992 landslide events (25 %) for validation. In the next step, the landslide-conditioning factors, namely slope angle, slope aspect, altitude, plan curvature, profile curvature, topographic wetness index (TWI), slope-length (LS), lithology, normalized difference vegetation index (NDVI), distance from rivers, distance from faults, distance from roads, and rainfall, were derived from the spatial database. Finally, landslide susceptibility maps of Ganzhou City were mapped in ArcGIS based on EBF, FR, Maxent, and LR approaches and were validated using the receiver operating characteristic (ROC) curve. The ROC plot assessment results showed that in the landslide susceptibility maps using the EBF, FR, Maxent, and LR models, the area under the curve (AUC) values were 0.7367, 0.7789, 0.7903, and 0.8237, respectively. Therefore, it can be concluded that all four models have AUC values of more than 0.70 and can be used in landslide susceptibility mapping in the study area. Also, the LR model had the best performance in the current study. Meanwhile, the mentioned models (EBF, FR, Maxent, and LR) showed almost similar results. The resultant susceptibility maps produced in the current study can be useful for land use planning and hazard mitigation purposes in the study area.

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Acknowledgments

The authors would like to thank the two anonymous reviewers and the journal editor for their helpful comments on the previous version of the manuscript.

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Authors and Affiliations

  1. Jiangxi Provincial Meteorological Observatory, Jiangxi Meteorological Bureau, No. 109 ShengfuBeier Road, Nanchang, 330046, China

    Haoyuan Hong

  2. Department of Watershed Management Engineering, College of Natural Resources, Tarbiat Modares University, Noor, Mazandaran, Iran

    Seyed Amir Naghibi

  3. Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran

    Hamid Reza Pourghasemi

  4. Department of Civil Engineering, Geospatial Information Science Research Center (GISRC), Faculty of Engineering, University Putra Malaysia, 43400, Serdang, Selangor, Malaysia

    Biswajeet Pradhan

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  1. Haoyuan Hong
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Correspondence to Hamid Reza Pourghasemi.

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Hong, H., Naghibi, S.A., Pourghasemi, H.R. et al. GIS-based landslide spatial modeling in Ganzhou City, China. Arab J Geosci 9, 112 (2016). https://doi.org/10.1007/s12517-015-2094-y

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