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Article A Comparison of Regression, Neural Network and Fuzzy Logic Models for Estimating Chlorophyll-A Concentrations in Reservoirs(Centre Environment Social & Economic Research Publ-ceser, 2005) Chen, Ding-Geng; Soyupak, SelcukA 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.Article Citation - WoS: 14Citation - Scopus: 14Fuzzy Logic Model To Estimate Seasonal Pseudo Steady State Chlorophyll-A Concentrations in Reservoirs(Springer, 2004) Soyupak, S; Chen, DGA fuzzy logic model is developed to estimate pseudo steady state chlorophyll-a concentrations in a very large and deep dam reservoir, namely Keban Dam Reservoir, which is also highly spatial and temporal variable. The estimation power of the developed fuzzy logic model was tested by comparing its performance with that from the classical multiple regression model. The data include chlorophyll-a concentrations in Keban lake as a response variable, as well as several water quality variables such as PO4 phosphorus, NO3 nitrogen, alkalinity, suspended solids concentration, pH, water temperature, electrical conductivity, dissolved oxygen concentration and Secchi depth as independent environmental variables. Because of the complex nature of the studied water body, as well as non-significant functional relationships among the water quality variables to the chlorophyll-a concentration, an initial analysis is conducted to select the most important variables that can be used in estimating the chlorophyll-a concentrations within the studied water body. Following the outcomes from this initial analysis, the fuzzy logic model is developed to estimate the chlorophyll-a concentrations and the advantages of this new model is demonstrated in model fitting over the traditional multiple regression method.
