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

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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 E**a 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 E**a 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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Authors and Affiliations

  1. Key Laboratory of Ecohydrology of Inland River Basin, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, People’s Republic of China

    Haijiao Yu, **aohu Wen, Qi Feng, Jianhua Si & Min Wu

  2. University of Chinese Academy of Sciences, Bei**g, 100049, People’s Republic of China

    Haijiao Yu & Min Wu

  3. School of Agricultural, Computational and Environmental Sciences, Institute of Agriculture and Environment (IAg&E), University of Southern Queensland, Springfield, QLD, 4300, Australia

    Ravinesh C. Deo

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  1. Haijiao Yu
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  4. Ravinesh C. Deo
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  5. Jianhua Si
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Correspondence to **aohu 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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