Application Research On Improved BP Neural Network For Water Quality Evaluation

Water is the source of life, and water environmental management has direct impact on humanity's survival and development. Water environmental quality assessment is the basis of water environmental management. The traditional evaluation methods, such as single-factor evaluation and comprehensive pollution evaluation, are questioned because of their application limitations. Therefore, it's very important for us to find an objective and universal water quality evaluation method. In recent years, the outstanding performance of BP neural network in pattern recognition makes it possible. The BP neural network used in water quality evaluation can overcome the shortcomings of traditional evaluation methods, and makes it possible for all kinds of rivers to compare water quality longitudinally. Because of the BP network's defects and the particularity of water quality assessment, the problems of work efficiency and recognition accuracy have not well resolved to the water quality assessment model based on BP network. To solve these problems, this paper researched into water quality assessment model based on improved BP neural network. Main works are as follows:(1)The basic theory of BP neural network was introduced. Taking the BP network's defects and its problems met in water quality assessment into account, the Golden Section Algorithm was improved to get the reasonable number of BP neural network's hidden nodes. Then the BP network was improved by the LM algorithm, and a water quality assessment model based on LM-BP network was established. The optimal model was used to evaluate the water quality degree of Xindu area of Chengdu, which were compared with the results derived from comprehensive pollution evaluation method. The feasibility of the water quality assessment model, founded by LM-BP neural network, was proved.(2)In order to further improve the recognition accuracy of the network, the Genetic Algorithm and BP network were combined. By using genetic algorithm's global search capability to find the optimal weights and threshold for the BP network, and the Water Quality Assessment based on GA-BP Network was established. Experiments indicate that the model's network performance (convergence speed and the mean square error of test samples) are better than the LM-BP network model's. Finally, the GA-BP network model was used to detect the same data as above, and the evaluation results were compared separately with the results from the LM-BP network model and the comprehensive pollution assessment method. The comparison showed that the GA-BP network water quality assessment model is more reasonable and practical than the model based on the LM-BP network.(3)In order to clarify the special relationship between the water quality indexes and water quality grades, linear interpolation was used to generate enough samples instead of random interpolation to train the founded GA-BP network. Comparing the test results, it was proved that the water evaluation grades can't reflect the basic pollution condition of the river. This indicated that the training samples generated by random interpolation well revealed the complex nonlinear relationship between the water quality indexes and the water quality grades.(4)All studies above showed that the GA-BP network water quality assessment model trained by samples generated by random interpolation has high recognition accuracy, practicality and versatility. Finally, the Graphical User Interface (GUI) of the water quality assessment model based on improved BP network was set up by MATLAB R2009a, which accelerated the progress of the BP network water quality assessment model from the theory to the practical application.