A neural network approach to multiobjective optimization for water quality management in a river basin

A new neural network-based multiobjective optimization of water quality management for water pollution control and river basin planning is presented. Past research on water quality management problems has shown that traditional multiobjective decision making does not provide an adequate solution since it depends directly on the decision maker's (DM's) preferences, which may not be clearly defined. In order to overcome the DM's preferences problem this study uses a neural network algorithm to form a model for the solution of the multiobjective problems of water quality management in a river basin. Using the backpropagation algorithm of feedforward neural networks, a multiobjective programming model can simulate the DM's preferences, providing direct help to the analysts involved in real applications. Before describing the details of the multiobjective optimization problem, neural networks are proposed which can be used to predict the DM's preference structures. To demonstrate the procedures and the performance of the neural network-based approach, the case of the Tou-Chen River Basin in Taiwan is selected for analysis and discussion.