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Modeling landslide susceptibility using data mining techniques of kernel logistic regression, fuzzy unordered rule induction algorithm, SysFor and random forest

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A Correction to this article was published on 01 January 2023

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Abstract

This paper introduces four advanced intelligent algorithms, namely kernel logistic regression, fuzzy unordered rule induction algorithm, systematically developed forest of multiple decision trees and random forest (RF), to perform the landslide susceptibility mapping in Jian’ge County, China, as well as well study of the connection between landslide occurrence and regional geo-environment characteristics. To start with, 262 landslide events were determined, and the proportion of randomly generated training data is 70%, while the proportion of randomly generated validation data is 30%, respectively. Then, through the comprehensive consideration of local geo-environment characteristics and relevant studies, fifteen conditioning factors were prepared, such as slope angle, slope aspect, altitude, profile curvature, plan curvature, sediment transport index, topographic wetness index, stream power index, distance to rivers, distance to roads, distance to lineaments, soil, land use, lithology and NDVI. Next, frequency ratio model was utilized to identify the corresponding relations for conditioning factors and landslides distribution. In addition, four data mining techniques were conducted to implement the landslide susceptibility research and generated landslide susceptibility maps. In order to examine and compare model performance, receiver operating characteristic curve was brought for judging accuracy of those four models. Finally, the results indicated that a traditional model, namely RF model, acquired the highest AUC value (0.859). Last but gained a lot of attention, the results can provide references for land use management and landslide prevention.

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Funding

This study is financially supported by Fundamental Research Funds for the Central Universities (300102351502), National Natural Science Foundation of China (211035210511), Shaanxi Province Natural Science Basic Research Program (2022JQ-457), Shaanxi Province Enterprises Talent Innovation Striving to Support the Plan (2021-1-2-1) and Inner scientific research project of Shaanxi Land Engineering Construction Group (DJNY2021-10, DJNY2022-16).

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Authors and Affiliations

  1. Key Laboratory of Degraded and Unused Land Consolidation Engineering, The Ministry of Natural Resources, Xi’an, Shaanxi, China

    Tingyu Zhang, Tianqing Chen & Na Lei

  2. Institute of Land Engineering and Technology, Shaanxi Provincial Land Engineering Construction Group Co., Ltd, Xi’an, Shaanxi, China

    Tingyu Zhang, Tianqing Chen & Na Lei

  3. Shaanxi Provincial Land Engineering Construction Group Land Survey Planning and Design Institute Co., Ltd., Xi’an, Shaanxi, China

    Quan Fu & Fangfang Liu

  4. Shaanxi Land Engineering Construction Group Co., Ltd., Xi’an, Shaanxi, China

    Chao Li

  5. Provincial Key Laboratory of Chang’an University, Shaanxi Key Laboratory of Land Consolidation, School of Land Engineering, Chang’an University, Xi’an, Shaanxi, China

    Huanyuan Wang

  6. School of Land Engineering, Chang’an University, Xi’an, Shaanxi, China

    Ling Han

  7. Earth Observation and Geoinformatics Division, National Institute for Space Research (INPE), Sao Jose dos Campos, São Paulo, Brazil

    Renata Pacheco Quevedo

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  1. Tingyu Zhang
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TZ and HW were involved in conceptualization, methodology, writing—review and editing, funding acquisition; QF helped in resources, software, validation; FL and CL contributed to formal analysis, data curation; CL, RPQ, TC and NL were involved in visualization; TZ and HW helped in writing—original draft preparation; LH helped in supervision.

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Correspondence to Huanyuan Wang.

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Zhang, T., Fu, Q., Li, C. et al. Modeling landslide susceptibility using data mining techniques of kernel logistic regression, fuzzy unordered rule induction algorithm, SysFor and random forest. Nat Hazards 114, 3327–3358 (2022). https://doi.org/10.1007/s11069-022-05520-7

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