Prediction of monthly regional groundwater levels through hybrid soft-computing techniques

doi:10.1016/j.jhydrol.2016.08.006

淡江大學機構典藏 > 工學院 > 水資源及環境工程學系暨研究所 > 期刊論文 > Item 987654321/109594

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题名:	Prediction of monthly regional groundwater levels through hybrid soft-computing techniques
作者:	Fi-John Chang;Li-Chiu Chang;Chien-Wei Huang;I-Feng Kao
关键词:	Regional groundwater level forecast;Artificial neural networks (ANNs);Self-organizing map (SOM);Nonlinear autoregressive with exogenous inputs (NARX) network;Zhuoshui River basin
日期:	2016-10
上传时间:	2017-02-24 02:12:12 (UTC+8)
出版者:	Elsevier BV
摘要:	Groundwater systems are intrinsically heterogeneous with dynamic temporal-spatial patterns, which cause great difficulty in quantifying their complex processes, while reliable predictions of regional groundwater levels are commonly needed for managing water resources to ensure proper service of water demands within a region. In this study, we proposed a novel and flexible soft-computing technique that could effectively extract the complex high-dimensional input–output patterns of basin-wide groundwater–aquifer systems in an adaptive manner. The soft-computing models combined the Self Organized Map (SOM) and the Nonlinear Autoregressive with Exogenous Inputs (NARX) network for predicting monthly regional groundwater levels based on hydrologic forcing data. The SOM could effectively classify the temporal-spatial patterns of regional groundwater levels, the NARX could accurately predict the mean of regional groundwater levels for adjusting the selected SOM, the Kriging was used to interpolate the predictions of the adjusted SOM into finer grids of locations, and consequently the prediction of a monthly regional groundwater level map could be obtained. The Zhuoshui River basin in Taiwan was the study case, and its monthly data sets collected from 203 groundwater stations, 32 rainfall stations and 6 flow stations during 2000 and 2013 were used for modelling purpose. The results demonstrated that the hybrid SOM-NARX model could reliably and suitably predict monthly basin-wide groundwater levels with high correlations (R2 > 0.9 in both training and testing cases). The proposed methodology presents a milestone in modelling regional environmental issues and offers an insightful and promising way to predict monthly basin-wide groundwater levels, which is beneficial to authorities for sustainable water resources management.
關聯:	Journal of Hydrology 541(B), p.965-976
DOI:	10.1016/j.jhydrol.2016.08.006
显示于类别:	[水資源及環境工程學系暨研究所] 期刊論文

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