A new approach to mathematical water quality modeling in reservoirs: Neural networks

Abstract

Neural Networks are becoming more and more valuable tools for system modeling and function approximation as computing power of microcomputers increase. Modeling of complex ecological systems such as reservoir limnology is very difficult since the ecological interactions within a reservoir are difficult to define mathematically and are usually system specific. To illustrate the potential use of Neural Networks in ecological modeling, a software was developed to train the data from Keban Dam Reservoir by backpropogation algorithm. Although the available data was insufficient and irregular, the system was trained successfully to estimate the chlorophyll-a concentration given the time, total suspended solids, total phosphorus, dissolved inorganic nitrogen and secchi depth. The model was quite successful in estimating the output with an average error of 0.01268 to 8.11612x10(-8) percent for the 5 sampling stations.