Earth Science Frontiers ›› 2016, Vol. 23 ›› Issue (3): 151-155.DOI: 10.13745/j.esf.2016.03.019

• Article • Previous Articles     Next Articles

Predicting the trends of pollutant concentrations in groundwater based on the combined method of the improved quantification theory and RBF artificial neural network

YANG Ping,WANG Xinmin,LU Laijun   

  1. 1. College of Earth Science, Jilin University, Changchun 130026, China
    2. Institute of Applied Mathematics, Changchun University of Technology, Changchun 130012, China
  • Received:2015-04-01 Revised:2015-07-21 Online:2016-05-15 Published:2016-05-15
  • Contact: 王新民(1957—),男,教授,主要从事水环境系统正反问题数学模型与数值方法的研究。

Abstract:

In this paper, an improved quantification theory I proposed by Chikio Hayashi was used as a preprocessing tool to covert quantitative data to qualitative data and to reduce data dimensionality for 20 factors impacting groundwater quality. Then 8 important characteristic factors were used as nodes of input layer in RBF Neural Networks, and RBF ANN model was created through training and learning the sampling data of monitoring well, finally migration and transformation law of pollutants were revealed. By using the combination model of the quantification theory and RBF ANN, the simulation results covered most of the existing experimental data and could forecast the dynamic changes of groundwater quality. The result is relatively accurate for a wide range and has some promotional value.

Key words: improved quantification theory I, reduce data dimensionality, characteristic factor, RBF ANN

CLC Number: