Title :
Using artificial neural network for reservoir water quality analysis in Taiwan
Author :
Yang, Chou-Ping ; Hsieh, Chi-Ying ; Wang, Yu-Min ; Hsiao, Wen-Ping
Author_Institution :
Center for Teaching Excellence, Nat. Pingtung Univ. of Sci. & Technol., Pingtung, Taiwan
Abstract :
Eutrophication has been considered as one of the most serious water quality problems of reservoirs in Taiwan. The back-propagation artificial neural network (ANN) was used to predict the water quality variation of the LungLuanTan Reservoir in southern Taiwan in current research. Three mathematical models were established and to predict the variation of parameters including total phosphorus (TP), secchi disk depth (SD), and dissolved oxygen (DO). The field data were divided into training and testing sets randomly. In all, the results indicated the model performing well to address the water quality and eutrophication problems found in reservoir. The artificial neural network is a valuable tool for reservoir water quality management.
Keywords :
backpropagation; environmental science computing; neural nets; quality management; reservoirs; water quality; LungLuanTan reservoir; backpropagation artificial neural network; dissolved oxygen; eutrophication problems; reservoir water quality management; secchi disk depth; total phosphorus; Artificial neural networks; Biological system modeling; Computational modeling; Predictive models; Quality management; Reservoirs; LungLuanTan Reservoir; artificial neural network; eutrophication; total phosphorus; water quality management;
Conference_Titel :
Consumer Electronics, Communications and Networks (CECNet), 2011 International Conference on
Conference_Location :
XianNing
Print_ISBN :
978-1-61284-458-9
DOI :
10.1109/CECNET.2011.5769286