Wavelet analysis–artificial neural network conjunction models for multi-scale monthly groundwater level predicting in an arid inland river basin, northwestern China

Wen, Xiaohu and Feng, Qi and Deo, Ravinesh C. and Wu, Min and Si, Jianhua (2017) Wavelet analysis–artificial neural network conjunction models for multi-scale monthly groundwater level predicting in an arid inland river basin, northwestern China. Hydrology Research: an International Journal, 48 (6). pp. 1710-1729. ISSN 1998-9563

Abstract

In this study, the ability of a wavelet analysis–artificial neural network (WA-ANN) conjunction model for multi-scale monthly groundwater level forecasting was evaluated in an arid inland river basin, northwestern China. The WA-ANN models were obtained by combining discrete wavelet transformation with ANN. For WA-ANN model, three different input combinations were trialed in order to optimize the model performance: (1) ancient groundwater level only, (2) ancient climatic data, and (3) ancient groundwater level combined with climatic data to forecast the groundwater level for two wells in Zhangye basin. Based on the key statistical measures, the performance of the WA-ANN model was significantly better than ANN model. However, WA-ANN model with ancient groundwater level as its input yielded the best performance for 1-month groundwater forecasts. For 2- and 3-monthly forecasts, the performance of the WA-ANN model with integrated ancient groundwater level and climatic data as inputs was the most superior. Notwithstanding this, the WA-ANN model with only ancient climatic data as its inputs also exhibited accurate results for 1-, 2-, and 3-month groundwater forecasting. It is ascertained that the WA-ANN model is a useful tool for simulation of multi-scale groundwater forecasting in the current study region.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: © IWA Publishing 2017.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 08 Jan 2017 23:25
Last Modified: 21 May 2018 00:05
Uncontrolled Keywords: wavelet analysis–artificial neural network conjunction model; multi-scale monthly groundwater level forecasting; China
Fields of Research : 01 Mathematical Sciences > 0104 Statistics > 010406 Stochastic Analysis and Modelling
04 Earth Sciences > 0401 Atmospheric Sciences > 040102 Atmospheric Dynamics
04 Earth Sciences > 0401 Atmospheric Sciences > 040107 Meteorology
04 Earth Sciences > 0401 Atmospheric Sciences > 040105 Climatology (excl.Climate Change Processes)
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
Identification Number or DOI: 10.2166/nh.2016.396
URI: http://eprints.usq.edu.au/id/eprint/30246

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