Estimating spatio-temporal dynamics of stream total phosphate concentration by soft computing techniques
學年 105
學期 1
出版（發表）日期 2016-08-15
作品名稱 Estimating spatio-temporal dynamics of stream total phosphate concentration by soft computing techniques
作品名稱（其他語言）
著者 Fi-John Chang; Pin-An Chen; Li-Chiu Chang; Yu-Hsuan Tsai
單位
出版者
著錄名稱、卷期、頁數 Science of The Total Environment 562, p.228-236
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.
關鍵字 Total phosphate (TP);Water qualityArtificial neural network (ANN);Nonlinear autoregressive with eXogenous input (NARX);networkGamma test
語言 en
ISSN 0048-9697
期刊性質 國外
收錄於 SCI EI
產學合作
通訊作者
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/987654321/116056 )