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Study on the influence of input variables on the supervised machine learning model for landslide susceptibility mapping

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Abstract

Supervised machine learning (ML) models are currently popular in landslide susceptibility mapping (LSM). However, the input variables of these models have some inherent limitations in terms of the lack of nonlinear relationship between the raw input variables and landslides, and the loss of a significant amount of information induced by the demand of the discretization of continuous environmental factors for the discrete and frequency ratio values input variables. Therefore, to address these issues, a new method of neighborhood frequency ratio for obtaining input variables was adopted in this paper. The present study compared the results of four input variables and seven supervised ML models under 28 conditions, with the use of ROC (receiver operating characteristic) curves as evaluation methods for the prediction results. The AUC (area under curve) values, ranging from 0.8223 to 0.9928, shows that the input variables are very important to the evaluation model. The experimental results were analyzed from the perspective of algorithm principles and data characteristics. The main conclusions are as follows: (1) for the non-tree models (i.e., models other than tree models), neighborhood frequency ratio of environmental factors should be used as the model inputs. (2) For tree models (i.e., decision trees and the decision tree based integrated models), the raw values of environmental factors can be used directly as the model inputs of the LSM model. (3) The decision tree based integrated models yielded better prediction results.

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Acknowledgements

We greatly thank the Professor Li Huizhong and Ye Shengsheng, Three Gorges Geotechnical Consultants Co., Ltd, for supplying the authors with the data of the landslides. This work is supported by the National Natural Science Foundation of China (no. 42107489), the Open Fund of Badong National Observation and Research Station of Geohazards (no. BNORSG-202304), the Natural Science Foundation of Hubei Province (no. 2022CFB557), the Open Fund of Key Laboratory of Geological Hazards on Three Gorges Reservoir Area (China Three Gorges University) of Ministry of Education (no. 2022KDZ14) and the 111 Project of Hubei Province (Grant number 2021EJD026).

Funding

Grant Recipient: Guo Fei the National Natural Science Foundation of China (No. 42107489), the Open Fund of Badong National Observation and Research Station of Geohazards (No. BNORSG-202304), the Natural Science Foundation of Hubei Province (No.2022CFB557), the Open Fund of Key Laboratory of Geological Hazards on Three Gorges Reservoir Area (China Three Gorges University) of Ministry of Education (No.2022KDZ14) and the 111 Project of Hubei Province (Grant Number 2021EJD026).

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Authors and Affiliations

  1. Key Laboratory of Geological Hazards on Three Gorges Reservoir Area, Ministry of Education, Yichang, 443002, China

    Peng Lai, Fei Guo, Xiaohu Huang, Dongwei Zhou, Li Wang & Guangfu Chen

  2. College of Civil Engineering and Architecture, China Three Gorges University, Yichang, 443002, China

    Peng Lai, Fei Guo, Xiaohu Huang, Dongwei Zhou, Li Wang & Guangfu Chen

  3. Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan, 430074, China

    Fei Guo

  4. Wuhan Center of China Geological Survey (Central South China Innovation Center for Geosciences), Wuhan, 430205, China

    Guangfu Chen

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  1. Peng Lai
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Author Contributions Conceptualization, Peng Lai, Fei Guo; Supervision, Fei Guo; Software, Peng Lai, Dongwei Zhou, Li Wang; Writing - original draft, Peng Lai, Xiaohu Huang, Guangfu Chen, Writing - review and editing, Fei Guo, Li Wang, Guangfu Chen. All authors reviewed the manuscript.

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Lai, P., Guo, F., Huang, X. et al. Study on the influence of input variables on the supervised machine learning model for landslide susceptibility mapping. Environ Earth Sci 83, 174 (2024). https://doi.org/10.1007/s12665-024-11501-9

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