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- Title
Comparative Study of Hybrid-Wavelet Artificial Intelligence Models for Monthly Groundwater Depth Forecasting in Extreme Arid Regions, Northwest China.
- Authors
Yu, Haijiao; Wen, Xiaohu; Feng, Qi; Deo, Ravinesh C.; Si, Jianhua; Wu, Min
- 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.
- Subjects
GROUNDWATER; WATER depth; ARTIFICIAL neural networks; DISCRETE wavelet transforms; WATER supply management
- Publication
Water Resources Management, 2018, Vol 32, Issue 1, p301
- ISSN
0920-4741
- Publication type
Article
- DOI
10.1007/s11269-017-1811-6