Comparative study of hybrid-wavelet artificial intelligence models for monthly groundwater depth forecasting in extreme arid regions, Northwest China
Article
| Article Title | Comparative study of hybrid-wavelet artificial intelligence models for monthly groundwater depth forecasting in extreme arid regions, Northwest China |
|---|---|
| ERA Journal ID | 30189 |
| Article Category | Article |
| Authors | Yu, Haijiao (Author), Wen, Xiaohu (Author), Feng, Qi (Author), Deo, Ravinesh C. (Author), Si, Jianhua (Author) and Wu, Min (Author) |
| Journal Title | Water Resources Management |
| Journal Citation | 32 (1), pp. 301-323 |
| Number of Pages | 23 |
| Year | 2018 |
| Publisher | Springer |
| Place of Publication | Netherlands |
| ISSN | 0920-4741 |
| 1573-1650 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/s11269-017-1811-6 |
| Web Address (URL) | https://link.springer.com/article/10.1007/s11269-017-1811-6 |
| 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 andWA-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 |
| ANZSRC Field of Research 2020 | 370108. Meteorology |
| 370510. Stratigraphy (incl. biostratigraphy, sequence stratigraphy and basin analysis) | |
| 460207. Modelling and simulation | |
| 469999. Other information and computing sciences not elsewhere classified | |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Chinese Academy of Sciences, China |
| Northwest Institute of Eco-Environment and Resources, China | |
| School of Agricultural, Computational and Environmental Sciences | |
| Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q46wy/comparative-study-of-hybrid-wavelet-artificial-intelligence-models-for-monthly-groundwater-depth-forecasting-in-extreme-arid-regions-northwest-china
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