Affiliations: State Key Laboratory of Water Environment Simulation,
School of Environmental, Beijing Normal University, Beijing 100875, China | Key Laboratory for Water and Sediment Sciences of
Ministry of Education, School of Environment, Beijing Normal University,
Beijing 100875, China
Abstract: The weekly water quality monitor data of Liuhai lakes between April
2003 and November 2004 in Beijing City were used as an example to build an
artificial neural networks (ANN) model and a multi-varieties regression model
respectively for predicting the fresh water algae bloom. The different
predicted abilities of the two methods in Liuhai lakes were compared. A
principle analysis method was first used to select the input variables of the
models to avoid the phenomenon of collinearity in the data. The results showed
that the input variables for the artificial neural networks were T, TP,
transparency(SD), DO, chlorophyll-a (Chl-a),pH and the output variable was
Chl-a. A three layer Levenberg-Marguardt feed forward learning algorithm in ANN
was used to model the eutrophication process of Liuhai lakes. 20 nodes in
hidden layer and 1 node of output for the ANN model had been optimized by trial
and error method. A sensitivity analysis of the input variables was performed
to evaluate their relative significance in determining the predicted values.
The correlation coefficient between predicted value and observed value in all
data and in test data were 0.717 and 0.816 respectively in the artificial
neural networks. The stepwise regression method was used to simulate the linear
relation between Chl-a and temperature, of which the correlation coefficient
was 0.213. By comparing the results of the two models, it was found that neural
network models were able to simulate non-linear behavior in the water
eutrophication process of Liuhai lakes reasonably and could successfully
estimate some extreme values from calibration and test data sets.
