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

Haijiao Yu, Xiaohu Wen (), Qi Feng, Ravinesh C. Deo (), Jianhua Si and Min Wu
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Haijiao Yu: Chinese Academy of Sciences
Xiaohu Wen: Chinese Academy of Sciences
Qi Feng: Chinese Academy of Sciences
Ravinesh C. Deo: University of Southern Queensland
Jianhua Si: Chinese Academy of Sciences
Min Wu: Chinese Academy of Sciences

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2018, vol. 32, issue 1, No 18, 323 pages

Abstract: 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.

Keywords: Discrete wavelet transform; Artificial neural network; Support vector regression; Groundwater level fluctuations; Extreme arid regions (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s11269-017-1811-6

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