Neural network models as a management tool in lakes

A research was made on the potential use of neural network based models in eutrophication modelling. As a result, an algorithm was developed to handle the practical aspects of designing, implementing and assessing the results of a neural network based model as a lake management tool. To illustrate t he advantages and limitations of the neural network model, a case study was carried out to estimate the chlorophyll-a concentration in Keban Dam Reser voir as a function of sampled water quality parameters (PO4 phosphorus, NO3 nitrogen, alkalinity, suspended solids concentration, pH, water temperatur e, electrical conductivity, dissolved oxygen concentration and Secchi depth ) by a neural network based model. Alternatively, the same system was solve d with a linear multiple regression model in order to compare the performan ces of the proposed neural network based model and the traditional linear m ultiple regression model. For both of the models, the linear correlation co efficients between the logarithms of observed and calculated chlorophyll-a concentrations were calculated. The correlation coefficient R, the best lin ear fit between the observed and calculated values, was evaluated to assess the performances of the two models. R values of 0.74 and 0.71 were obtaine d for the neural network based model and the linear multiple regression mod el, respectively. The study showed that the neural network based model can be used to estimate chlorophyll-a with a performance similar to that of the traditional linear multiple regression method. However, for cases where th e input and the output variables are not linearly correlated, neural networ k based models are expected to show a better performance.