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Volume 39 Issue 4
Aug.  2020
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Article Navigation >  CARSOLOGICA SINICA  > 2020  >  39(4): 622-628
ZHOU Aihong, NIU Pengfei, YUAN Ying, HUANG Hucheng. Prediction of karst surface subsidence risk in the Fankou lead-zinc mine area based on PCA-PSO-SVM[J]. CARSOLOGICA SINICA, 2020, 39(4): 622-628. doi: 10.11932/karst2020y30
Citation: ZHOU Aihong, NIU Pengfei, YUAN Ying, HUANG Hucheng. Prediction of karst surface subsidence risk in the Fankou lead-zinc mine area based on PCA-PSO-SVM[J]. CARSOLOGICA SINICA, 2020, 39(4): 622-628. doi: 10.11932/karst2020y30
shu

Prediction of karst surface subsidence risk in the Fankou lead-zinc mine area based on PCA-PSO-SVM

doi: 10.11932/karst2020y30
  • 1.

    School of Urban Geology and Engineering， Hebei Geologic University/Center of Applied Technology Development for Eco-environmental Geology， Hebei Geologic University

  • 2.

    School of Urban Geology and Engineering， Hebei Geologic University

  • 3.

    Shanxi Institute of Geological Survey

  • Publish Date: 2020-08-25
    Abstract
  • Karst surface subsidence is a dynamic geological phenomenon with the characteristics of concealment and suddenness， which results from the joint effect many factors. Thus， it is difficult to accurately predict the risk of surface subsidence by the conventional simple mathematical model. In this paper， the Principal Component Analysis （PCA） is used to extract five principal components from 11 influencing factors， including groundwater level， fluctuation range of groundwater level and water supply， so as to make a new interpretation of the principal components leading to the risk of surface subsidence. Additionally， the Support Vector Machine （SVM） method optimized by Particle Swarm Optimization （PSO） is introduced to establish a PCA-PSO-SVM model for prediction of risk of karst surface subsidence. Finally， combined with the engineering example of the Fankou lead-zinc mine， the prediction results by the above proposed model are compared with those obtained by the single SVM model. The results show that the PCA-PSO-SVM risk prediction model has higher accuracy， which can provide a basis for prevention and control of karst surface subsidence.

     

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