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. and Si, Jianhua and Wu, Min (2017) Comparative study of hybrid-wavelet artificial intelligence models for monthly groundwater depth forecasting in extreme arid regions, Northwest China. Water Resources Management. 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: Published online 23 September 2017. Permanent restricted access to ArticleFirst version, in accordance with the copyright policy of the publisher. 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 / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 25 Sep 2017 04:24
Last Modified: 02 May 2018 00:28
Uncontrolled Keywords: discrete wavelet transform, artificial neural network, support vector regression, groundwater level fluctuations, extreme arid regions
Fields of Research : 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 Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970104 Expanding Knowledge in the Earth Sciences
Identification Number or DOI: 10.1007/s11269-017-1811-6
URI: http://eprints.usq.edu.au/id/eprint/33139

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