https://scholars.lib.ntu.edu.tw/handle/123456789/448933
標題: | Estimating spatio-temporal dynamics of stream total phosphate concentration by soft computing techniques | 作者: | FI-JOHN CHANG Chen P.-A. Chang L.-C. Tsai Y.-H. |
關鍵字: | Artificial neural network (ANN); Gamma test; Nonlinear autoregressive with eXogenous input (NARX) network; Total phosphate (TP); Water quality | 公開日期: | 2016 | 卷: | 562 | 起(迄)頁: | 228-236 | 來源出版物: | Science of the Total Environment | 摘要: | This study attempts to model the spatio-temporal dynamics of total phosphate (TP) concentrations along a river for effective hydro-environmental management. We propose a systematical modeling scheme (SMS), which is an ingenious modeling process equipped with a dynamic neural network and three refined statistical methods, for reliably predicting the TP concentrations along a river simultaneously. Two different types of artificial neural network (BPNN-static neural network; NARX network-dynamic neural network) are constructed in modeling the dynamic system. The Dahan River in Taiwan is used as a study case, where ten-year seasonal water quality data collected at seven monitoring stations along the river are used for model training and validation. Results demonstrate that the NARX network can suitably capture the important dynamic features and remarkably outperforms the BPNN model, and the SMS can effectively identify key input factors, suitably overcome data scarcity, significantly increase model reliability, satisfactorily estimate site-specific TP concentration at seven monitoring stations simultaneously, and adequately reconstruct seasonal TP data into a monthly scale. The proposed SMS can reliably model the dynamic spatio-temporal water pollution variation in a river system for missing, hazardous or costly data of interest. © 2016 Elsevier B.V. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/448933 | ISSN: | 0048-9697 | DOI: | 10.1016/j.scitotenv.2016.03.219 | SDG/關鍵字: | Environmental management; Face recognition; Neural networks; Rivers; Soft computing; Water pollution; Water quality; Water resources; Dynamic neural networks; Gamma test; Non-linear autoregressive with exogenous; Phosphate concentration; Seasonal water qualities; Softcomputing techniques; Spatio-temporal dynamics; Total phosphate (TP); River pollution; phosphate; nitrogen; phosphate; phosphorus; artificial neural network; concentration (composition); modeling; phosphate; spatiotemporal analysis; statistical analysis; streamwater; water quality; Article; artificial neural network; concentration (parameters); controlled study; dynamics; environmental management; environmental monitoring; network dynamic neural network; priority journal; river; seasonal variation; spatiotemporal analysis; static neural network; statistical analysis; stream (river); systematical modeling scheme; Taiwan; water analysis; water management; water pollution; water quality; analysis; chemistry; procedures; spatiotemporal analysis; statistics and numerical data; water pollutant; Dahan River; Taiwan; Environmental Monitoring; Neural Networks (Computer); Nitrogen; Phosphates; Phosphorus; Rivers; Spatio-Temporal Analysis; Water Pollutants, Chemical; Water Pollution |
顯示於: | 生物環境系統工程學系 |
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