Research On Water Quality Evaluation Model Based On BP Neural Network

As a drought and water-scarce country,China also has serious water pollution problems.Water quality evaluation is to evaluate the quality grade of water resources.The water quality environment is complex and changeable,and there are a large number of microorganisms and chemicals,which increase the difficulty of water quality evaluation.BP neural network is one of the most widely used neural network models at this stage.Its adaptability,self-learning and distributed processing capabilities have been well applied in water quality evaluation.But the BP neural network has some shortcomings,which will lead to inaccurate evaluation results.Therefore,it is necessary to find a better water quality evaluation method.This paper conducts experiments and research on the water quality evaluation model based on BP neural network,hoping to find a better water quality evaluation method.In the research,it is found that genetic algorithm has a powerful macro search function,which can solve the local extreme value problem of BP neural network,and adaptive genetic algorithm can improve the convergence speed and optimize the learning effect.In the experiment,the BP neural network and the BP neural network optimized by the above two methods were respectively used to evaluate the water quality.The results show that the BP neural network optimized by the adaptive genetic algorithm has achieved a good evaluation effect on the water quality..It is concluded that the use of adaptive genetic algorithm to optimize the original water quality evaluation method can better evaluate the water quality of various watersheds in China,and it has a good applicable value in water quality evaluation,providing a data basis for water resources protection.