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Ground fissure susceptibility mapping based on factor optimization and support vector machines

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Abstract

For the purpose of batter evaluating ground fissure susceptibility (GFS), this study developed a hybrid model based on factor optimization and support vector machines (SVM). Firstly, an evaluation index system of GFS was established containing 15 influence factors. Then, the data sample was normalized by certainty factors (CF) for the preparation of data analysis and machine learning. In the process of factor optimization, Schmidt orthogonalization (SO) was used to reduce collinearity in the data, and Pearson correlation coefficient (PCC) analysis was utilized to estimate its effect. Following, the principal component analysis (PCA) was applied to integrate and reduce the dimension of samples, and 9 component vectors were selected for the construction of SVM eventually. In the procedure of SVM modeling, the modeling data set was composed of 140 land fissure data and 140 non-deformation points. The cross-validation (CV) method was adopted to determine the required parameters, and the performance of the completed model was verified by statistical analysis and receiver operating characteristic (ROC) curve. The prediction accuracy of the final model reached a level of 0.852 with selected parameters. Finally, the GFS values of whole data in the study area were calculated by the trained model, and the GFS map was produced with its effect assessed. In conclusion, the model in this research can evaluate the GFS accurately and has a high value in both scientific research and engineering application.

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Acknowledgements

The financial support is gratefully acknowledged. The authors also thank the editor and the anonymous reviewers for their insightful and constructive comments.

Funding

This work was funded by the China Geological Survey (No. DD20160267; No. DD20190317), the China Postdoctoral Science Foundation funded project (2021M700608) and the Natural Science Foundation of Chongqing, China (cstc2021jcyj-bsh0047).

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Authors and Affiliations

  1. Graduate School of Chinese Academy of Geological Sciences, Beijing, 100081, China

    Xuebing Wang

  2. China Institute of Geological Environment Monitoring, Beijing, 100081, China

    Xuebing Wang

  3. Chongqing 208 Institute of Geological Environment Co. Ltd., Chongqing, 400700, China

    Xuebing Wang, Zhihua Zhang & Jian Guo

  4. School of Civil Engineering, Chongqing University, Chongqing, 400045, China

    Luqi Wang & Wengang Zhang

  5. Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing, 100081, China

    Chunshan Zhang & Chengxuan Tan

  6. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China

    Pei Yan

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  1. Xuebing Wang
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Correspondence to Luqi Wang.

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Wang, X., Wang, L., Zhang, W. et al. Ground fissure susceptibility mapping based on factor optimization and support vector machines. Bull Eng Geol Environ 81, 341 (2022). https://doi.org/10.1007/s10064-022-02843-4

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