Estimation of chlorophyll a concentration in an eutrophic lake with artificial neural network models
2007
Sai, K.(Kyushu Univ., Fukuoka (Japan)) | Harada, M. | Yoshida, I. | Hiramatsu, K. | Mori, M.
An artificial neural network model with three-layer structure was applied to estimate chlorophyll a concentration in an eutrophic lake. First input variables, which resulted in high calibration accuracy, were searched. As a result calibration accuracy was highest when input variables were set to TN, TP, DO, water temperature, solar radiation, air temperature, wind velocity, and Wedderburn number. This result means that the model incorporated the relationship between chlorophyll a concentration and the meteorological, hydraulic, and aquatic factors into the network structure. Next, the feasibility of the estimation of chlorophyll a concentration was examined by the model. As a result, chlorophyll a concentration could not be sufficiently estimated by this model. To improve estimation accuracy, network structure was reconstructed by considering the time history of the variation of the meteorological and water quality data for the previous 24 hours and incorporating such data into the input variables. The result showed that the estimation accuracy was remarkably improved.
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