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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/109594

    Title: Prediction of monthly regional groundwater levels through hybrid soft-computing techniques
    Authors: Fi-John Chang;Li-Chiu Chang;Chien-Wei Huang;I-Feng Kao
    Keywords: Regional groundwater level forecast;Artificial neural networks (ANNs);Self-organizing map (SOM);Nonlinear autoregressive with exogenous inputs (NARX) network;Zhuoshui River basin
    Date: 2016-10
    Issue Date: 2017-02-24 02:12:12 (UTC+8)
    Publisher: Elsevier BV
    Abstract: 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.
    Relation: Journal of Hydrology 541(B), p.965-976
    DOI: 10.1016/j.jhydrol.2016.08.006
    Appears in Collections:[水資源及環境工程學系暨研究所] 期刊論文

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