Journal of Jilin University(Earth Science Edition) ›› 2016, Vol. 46 ›› Issue (5): 1511-1519.doi: 10.13278/j.cnki.jjuese.201605207

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Evaluating Mine Geology Environmental Quality Using Improved SVM Method

Lu Wenxi1,2, Guo Jiayuan1,2, Dong Haibiao1,2, Zhang Yu1,2, Lin Lin1,2   

  1. 1. Key Laboratory of Groundwater Resources and Environment of Ministry of Education, Jilin University, Changchun 130021, China;
    2. College of Environment and Resources, Jilin University, Changchun 130021, China
  • Received:2016-02-03 Online:2016-09-26 Published:2016-09-26
  • Supported by:

    Supported by China Geological Survey Bureau Projects(1212011140027,12120114027401)and Jilin University Postgraduate Innovation Fund Project(2015083)

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Abstract:

The grid search method, as on kind of traditional evaluation model of SVM, can be influenced by subjective factors on selecting parameters. So in this study, particle swarm algorithm was chosen to optimize the SVM model and optimized SVM model (PSO-SVM) was used to evaluate geological environment of 135 mines in the Chang-Ji-Tu economic zone. It showed that the evaluation results were almost consistent with comprehensive evaluation results, the similiarity reached to 95.56%. Compared to traditional SVM, itreached to 91.11%. Based on the actual situation and comprehensive analysis of three kinds of evaluation results, we found out that PSO-SVM evaluation results were more in line with the actual situation. Optimized SVM model could effectively avoid the influence of human factors and improve the level of mine geological environment assessment, it was feasible and effective in the evaluation. Evaluation results based on the optimized SVM model showed that mine geological environment was affected by human activities in the study area such as mining, 54.1% of the mine suffered serious damage (III grade), 25.9% suffered moderate damage (II level). The evaluation results can provide decisions for environmental restoration in the study area.

Key words: support vector machine (SVM), particle swarm optimization (PSO), mine geological environment quality evaluation, Chang-Ji-Tu economic zone

CLC Number: 

  • P69

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