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Comparative Study of Hybrid-Wavelet Artificial Intelligence Models for Monthly Groundwater Depth Forecasting in Extreme Arid Regions, Northwest China

Abstract

Prediction of groundwater depth (GWD) is a critical task in water resources management. In this study, the practicability of predicting GWD for lead times of 1, 2 and 3 months for 3 observation wells in the Ejina Basin using the wavelet-artificial neural network (WA-ANN) and wavelet-support vector regression (WA-SVR) is demonstrated. Discrete wavelet transform was applied to decompose groundwater depth and meteorological inputs into approximations and detail with predictive features embedded in high frequency and low frequency. WA-ANN and WA-SVR relative of ANN and SVR were evaluated with coefficient of correlation (R), Nash-Sutcliffe efficiency (NS), mean absolute error (MAE), and root mean squared error (RMSE). Results showed that WA-ANN and WA-SVR have better performance than ANN and SVR models. WA-SVR yielded better results than WA-ANN model for 1, 2 and 3-month lead times. The study indicates that WA-SVR could be applied for groundwater forecasting under ecological water conveyance conditions.

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Acknowledgments

This research was supported by the National Natural Sciences Foundation of China (31370466). The data were acquired from obtained from Chinese Meteorological Data Sharing Service System and Ejina Banner Water Authority. Dr. RC Deo appreciates the support from University of Southern Queensland that provided the Research Activation Incentive Scheme grant (RAIS, July – September 2015) provided to establish collaborative links with the Cold and Arid Regions Environmental and Engineering Institute Chinese at Chinese Academy of Sciences. We thank the Reviewer, Associate Editor and the Editor-In-Chief for their critical comments for this research paper.

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Correspondence to Xiaohu Wen or Ravinesh C. Deo.

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Yu, H., Wen, X., Feng, Q. et al. Comparative Study of Hybrid-Wavelet Artificial Intelligence Models for Monthly Groundwater Depth Forecasting in Extreme Arid Regions, Northwest China. Water Resour Manage 32, 301–323 (2018). https://doi.org/10.1007/s11269-017-1811-6

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Keywords

  • Discrete wavelet transform
  • Artificial neural network
  • Support vector regression
  • Groundwater level fluctuations
  • Extreme arid regions