JOURNAL OF YANGTZE RIVER SCIENTIFIC RESEARCH INSTI ›› 2016, Vol. 33 ›› Issue (8): 18-21.DOI: 10.11988/ckyyb.20150474

• WATER RESOURCES AND ENVIRONMENT • Previous Articles     Next Articles

A Wavelet-ANN Hybrid Model for Groundwater Level Forecasting

ZHANG Jian-feng1, 2, 3, LIU Jian-bao1,3, CUI Shu-jun1, 2, 3, XIE Yu-hua1   

  1. 1.School of Resources and Environmental Engineering, Henan University of Engineering, Zhengzhou 451191,
    China; 2.Zhengzhou Key Laboratory of Geological Hazard and Prevention of Mine, Zhengzhou 451191, China;
    3.Research Center of Engineering and Technology for Henan College Geological Hazard and Prevention of Coal Mine, Zhengzhou 451191, China
  • Received:2015-06-04 Revised:2015-08-10 Online:2016-07-25 Published:2016-07-25

Abstract: Due to over-exploitation of groundwater in many cities of North China Plain, there is a tendency of lasting decrease in groundwater level, which results in serious problems, such as groundwater exhaustion, land subsidence and seawater intrusion. In order to accurately predict changes of urban groundwater level, based on artificial neural network (ANN) and analysis of multi-scale of wavelet transform (WT), we established a wavelet-ANN conjugate model and test its accuracy to predict groundwater level. Measured data of groundwater level at Pinggu district of Beijing were taken as research objects. We predicted groundwater levels at the district by back propagation (BP) model and hybrid model. Then, we calculated the prediction accuracy by using statistical parameters including root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (R). Results showed that the MAE of the hybrid model from the first month to the third month was 0.535, 0.598 and 0.634 m, respectively, whereas 0.566, 0.824 and 0.940 m for BP model. The MAE of hybrid model from the first month to the third month was 95%, 73% and 67% of that of BP model, respectively. Comparison of results reveals that the hybrid model has advantages of better prediction accuracy and longer effective prediction duration.

Key words: North China Plain, over-exploitation, groundwater level, discrete wavelet transform, artificial neural network, forecasting

CLC Number: