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Landslide susceptibility mapping using statistical bivariate models and their hybrid with normalized spatial-correlated scale index and weighted calibrated landslide potential model

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Abstract

Considering the slope units as our reference mapping units, three statistical models [frequency ratio (FR), index of entropy (IOE), and evidential belief function (EBF)] are used in combination by two methods [normalized spatial-correlated scale index (NSCI) and weighted calibrated landslide potential model (WCLPM)]. For this aim, ten conditioning factors correlated with landslide namely, altitude, slope angle, slope aspect, relief amplitude, cutting depth, gully density, surface roughness, distance to roads, rainfall, and lithology are considered. The performance of the models is tested using the area under the receiver operating characteristic (ROC) curve (AUC) and several statistical evaluation measures. The weighted calibrated landslide potential index (WCLPI)-based FR model has the highest predictive capability, followed by the calibrated landslide potential index (CLPI)-based FR, the WCLPI-EBF, the CLPI-EBF, the WCLPI-IOE, the CLPI-IOE, the FR, the EBF, and the IOE models, respectively. Results indicated that hybrid models have improved significantly the performance of single models. This highlights that NSCI and WCLPM hybrid techniques are promising methods for landslide susceptibility assessment.

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Acknowledgements

This work is financially supported by the China Postdoctoral Science Foundation (2020M680583), the National Postdoctoral Program for Innovative Talent of China (BX20200191), the Excellent Sino-foreign Youth Exchange Program of China Association for Science and Technology in 2020 (No. 58), and the Shuimu Tsinghua Scholar Program (2019SM058).

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Authors and Affiliations

  1. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 611756, China

    Zhuo Chen

  2. State Key Laboratory of Hydraulic and Mountain River Engineering, Department of Geotechnical Engineering, Sichuan University, Chengdu, 610065, China

    Zhuo Chen

  3. State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, 100084, China

    Danqing Song

  4. Department of Ecology and Water Resources Management, Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, Tashkent, Uzbekistan

    Mukhiddin Juliev

  5. Turin Polytechnic University in Tashkent, 100000, Tashkent, Uzbekistan

    Mukhiddin Juliev

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

    Hamid Reza Pourghasemi

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Correspondence to Danqing Song.

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Chen, Z., Song, D., Juliev, M. et al. Landslide susceptibility mapping using statistical bivariate models and their hybrid with normalized spatial-correlated scale index and weighted calibrated landslide potential model. Environ Earth Sci 80, 324 (2021). https://doi.org/10.1007/s12665-021-09603-9

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