期刊論文
| 學年 | 105 |
|---|---|
| 學期 | 1 |
| 出版（發表）日期 | 2016-10-01 |
| 作品名稱 | Prediction of monthly regional groundwater levels through hybrid soft-computing techniques |
| 作品名稱（其他語言） | |
| 著者 | Fi-John Chang; Li-Chiu Chang; Chien-Wei Huang; I-Feng Kao |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Journal of Hydrology 541(B), p.965-976 |
| 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. |
| 關鍵字 | Regional groundwater level forecast;Artificial neural networks (ANNs);Self-organizing map (SOM);Nonlinear autoregressive with exogenous inputs (NARX) network;Zhuoshui River basin |
| 語言 | en |
| ISSN | 0022-1694 |
| 期刊性質 | 國外 |
| 收錄於 | SCI EI |
| 產學合作 | 國內 |
| 通訊作者 | Fi-John Chang |
| 審稿制度 | 是 |
| 國別 | NLD |
| 公開徵稿 | |
| 出版型式 | ,電子版,紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/109594 ) |