Prediction of the concentration of chlorophyll-a for Liuhai urban lakes in Beijing City

• Published in: Journal of environmental sciences (China) Vol. 18; no. 4; pp. 827 - 831
• Main Authors: Zeng, Yong, Yang, Zhi-feng, Liu, Jing-ling
• Format: Journal Article
• Language: English
• Published: Netherlands Key Laboratory for Water and Sediment Sciences of Ministry of Education, School of Environment, Beijing Normal University, Beijing 100875, China 2006; State Key Laboratory of Water Environment Simulation, School of Environmental, Beijing Normal University, Beijing 100875, China
• Subjects: China; Chlorophyll - analysis; Chlorophyll - metabolism; Environmental Monitoring - methods; Eutrophication; Fresh Water; Freshwater; Linear Models; Models, Biological; Neural Networks (Computer); Predictive Value of Tests; Principal Component Analysis - methods; Temperature; China; China, People's Rep., Beijing; artificial neural networks; forecast; eutrophication; Beijing City; multi-varieties regression; Liuhai lakes
• ISSN: 1001-0742
• DOI: 10.3321/j.issn:1001-0742.2006.04.035

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.