Nonlinear Steady State Modeling for River Quality by a Neural Network with a Self-Selection Ability of Network Structure
This paper deals with a nonlinear steady state modeling of river quality by a neural network which has a self-selection ability of network structure. This neural network algorithm is a revised one of the neural network which can identify a nonlinear system whose structure is very large and complex. By using measured data of river quality such as BOD and DO concentrations, a nonlinear steady state model of river quality is identified by the neural network and the results are compared with the results which are obtained by a physical model and a GMDH model. And it is shown that the neural network in this paper gives better prediction results as compared with a physical model and a GMDH model.