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Novel hybrid artificial intelligence approach of bivariate statistical-methods-based kernel logistic regression classifier for landslide susceptibility modeling

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Abstract

Globally, and in China, landslides constitute one of the most important and frequently encountered natural hazard events. In the present study, landslide susceptibility evaluation was undertaken using novel ensembles of bivariate statistical-methods-based (evidential belief function (EBF), statistical index (SI), and weights of evidence (WoE)) kernel logistic regression machine learning classifiers. A landslide inventory comprising 222 landslides and 15 conditioning factors (slope angle, slope aspect, altitude, plan curvature, profile curvature, stream power index, sediment transport index, topographic wetness index, distance to rivers, distance to roads, distance to faults, NDVI, land use, lithology, and rainfall) was prepared as the spatial database. Correlation analysis and selection of conditioning factors were conducted using multicollinearity analysis and classifier attribute evaluation methods, respectively. The receiver operating characteristic curve method was used to validate the models. The areas under the success rate (AUC_T) and prediction rate (AUC_P) curves and landslide density analysis were also used to assess the prediction capability of the landslide susceptibility maps. Results showed that the EBF-KLR hybrid model had the highest predictive capability in landslide susceptibility assessment (AUC values of 0.814 and 0.753 for the training and validation datasets, respectively; AUC_T value of 0.8511 and AUC_P value of 0.7615), followed in descending order by the SI-KLR and WoE-KLR hybrid models. These findings indicate that hybrid models could improve the predictive capability of bivariate models, and that the EBF-KLR is a promising hybrid model for the spatial prediction of landslides in susceptible areas.

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Acknowledgments

This research was supported by the International Partnership Program of Chinese Academy of Sciences (Grant No. 115242KYSB20170022), the National Natural Science Foundation of China (Grant No. 41807192), China Postdoctoral Science Foundation (Grant No. 2018 T111084, 2017 M613168), Project funded by Shaanxi Province Postdoctoral Science Foundation (Grant No. 2017BSHYDZZ07), and the Open Fund of Shandong Provincial Key Laboratory of Depositional Mineralization & Sedimentary Minerals (Grant No. DMSM2017029). We thank James Buxton MSc from Edanz Group (www.edanzediting.com./ac) for editing a draft of this manuscript.

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Authors and Affiliations

  1. College of Geology & Environment, Xi’an University of Science and Technology, Xi’an, 710054, Shaanxi, China

    Wei Chen

  2. Shandong Provincial Key Laboratory of Depositional Mineralization & Sedimentary Minerals, Shandong University of Science and Technology, Qingdao, 266590, China

    Wei Chen

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

    Himan Shahabi

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

    Ataollah Shirzadi

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

    Haoyuan Hong, Junzhi Liu & A-Xing Zhu

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

    Haoyuan Hong, Junzhi Liu & A-Xing Zhu

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

    Haoyuan Hong, Junzhi Liu & A-Xing Zhu

  8. Geological Engineering Department, Karadeniz Technical University, Trabzon, Turkey

    Aykut Akgun

  9. Key Laboratory of Active Tectonics and Volcano, Institute of Geology, China Earthquake Administration, #1 Huayanli, Chaoyang District, PO Box 9803, Beijing, 100029, People’s Republic of China

    Yingying Tian

  10. State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, 430071, Hubei, China

    Shaojun Li

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  1. Wei Chen
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Chen, W., Shahabi, H., Shirzadi, A. et al. Novel hybrid artificial intelligence approach of bivariate statistical-methods-based kernel logistic regression classifier for landslide susceptibility modeling. Bull Eng Geol Environ 78, 4397–4419 (2019). https://doi.org/10.1007/s10064-018-1401-8

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