Comparative study of hybrid-wavelet artificial intelligence models for monthly groundwater depth forecasting in extreme arid regions, Northwest China

Yu, Haijiao and Wen, Xiaohu and Feng, Qi and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 and Si, Jianhua and Wu, Min (2018) Comparative study of hybrid-wavelet artificial intelligence models for monthly groundwater depth forecasting in extreme arid regions, Northwest China. Water Resources Management, 32 (1). pp. 301-323. ISSN 0920-4741


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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: 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.
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019)
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019)
Date Deposited: 25 Sep 2017 04:24
Last Modified: 31 Mar 2021 04:28
Uncontrolled Keywords: discrete wavelet transform, artificial neural network, support vector regression, groundwater level fluctuations, extreme arid regions
Fields of Research (2008): 04 Earth Sciences > 0401 Atmospheric Sciences > 040107 Meteorology
04 Earth Sciences > 0403 Geology > 040301 Basin Analysis
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970104 Expanding Knowledge in the Earth Sciences
Identification Number or DOI: https://doi.org/10.1007/s11269-017-1811-6
URI: http://eprints.usq.edu.au/id/eprint/33139

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