Estimating spatio-temporal dynamics of stream total phosphate concentration by soft computing techniques.
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.
['Chang|Fi-John|FJ|', 'Chen|Pin-An|PA|', 'Chang|Li-Chiu|LC|', 'Tsai|Yu-Hsuan|YH|']
D004784:Environmental Monitoring D016571:Neural Networks, Computer D009584:Nitrogen D010710:Phosphates D010758:Phosphorus D045483:Rivers D062211:Spatio-Temporal Analysis D014874:Water Pollutants, Chemical D014876:Water Pollution
Total phosphate (TP) Water quality Artificial neural network (ANN) Gamma test Nonlinear autoregressive with eXogenous input (NARX) network