APPLICATION OF ARTIFICIAL NEURAL NETWORK FOR WATER QUALITY MANAGEMENT

Abstract

A new artificial neural network based on decision-making approach for water quality management to control environmental pollution is presented. Previous research on water quality management problems has shown that traditional optimization techniques and an expert-system approach do not provide an educated solution comparing with decision making approach, which is related to the interpretation of data based on certain set of rules. Under such conditions, the Artificial Neural Network (ANN) learns the rule governing the decision-making through a series of experiments. In the present study, ANN was used to evaluate the relative effects of various pollution sources on the quality of river water. Using a backpropagation algorithm of a feed forward neural network, the relative effects of pollution sources were evaluated for strategic planning of water quality management. The case study for the Hanjiang River of China was selected to demonstrate the procedure and performance of a neural network-based approach for analysis and discussion.