A Comparison of Regression, Neural Network and Fuzzy Logic Models for Estimating Chlorophyll-A Concentrations in Reservoirs

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2005

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Centre Environment Social & Economic Research Publ-ceser

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Abstract

A comparison is conducted in this paper for the multiple linear regression, neural network and fuzzy logic models for their ability to estimate pseudo steady state chlorophyll-a concentrations in a very large and deep dam reservoir that exhibits high spatial and temporal variability. The utilized data set include chlorophyll-a concentrations as an indicator of primary productivity as well as several other water quality variables such as alkalinity, PO4 phosphorus, water temperature and dissolved oxygen concentrations as independent environmental variables. Using the conventional model criteria of correlation coefficient and mean square errors, the fuzzy logic model performed the best with the neural network model better than multiple linear regression model.

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Chen, Ding-Geng/0000-0002-3199-8665

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Multiple linear regression model, fuzzy logic model, neural network model, dam reservoir management, eutrophication

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0

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Volume

3

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1

Start Page

65

End Page

78

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