Establishing a Predictive Model for Chlorophyll-A Concentration in Lake Daechung, Korea Using Multilinear Statistical Techniques
Publication: Journal of Environmental Engineering
Volume 141, Issue 2
Abstract
Multilinear statistical techniques were used to establish a predictive model for chlorophyll-a concentration in Daechung reservoir, Korea. By relating water quality, weather data, and various hydrological parameters to the chlorophyll-a concentration, a correlation was able to be established between them. statistical modeling and data mining [model tree, artificial neural network (ANN), and radial basis function (RBF)] with Weka were used specifically employed for the purpose. An empirical model was developed with and without normalization of the input data. The empirical model with normalization of the input data showed excellent correlations () as compared to that without normalization of the input data (). Based on 10 years of data and selection attributes using Weka preprocess, COD, T-P, and were deemed to be the best attributes for the prediction of chlorophyll-a concentration in Daechung reservoir. As compared to classical mechanistic modeling, this approach is significantly less complicated, less time-consuming and more cost effective.
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Published In
Journal of Environmental Engineering
Volume 141 • Issue 2 • February 2015
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Oct 19, 2013
Accepted: Jul 11, 2014
Published online: Sep 11, 2014
Published in print: Feb 1, 2015
Discussion open until: Feb 11, 2015
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