Research On Water Quality Evaluation And Intelligent Prediction Method Of Water Bloom For Lakes And Reservoir

With the rapid development of social economy, water environment has been aroused more and more widely attention, Eutrophication and algae blooms as a typical problems of the water environment, which has become one of most research subject for environmental protection field.Firstly, the research background, purpose and significance, research situation and the developing trend are introduced. Summarize several common evaluation of eutrophication, according to current problems, a fuzzy synthesis evaluation method based on genetic algorithm are presented. Then the evaluation index and evaluation standard of water quality are determined, calculating fuzzy membership matrix accord to eutrophication state standard, the weight model of water quality parameters base on genetic algorithm is established, reaching the comprehensive evaluation value by simulation. The result show the reasonable of this method and offers reference for water environment treatment.Secondly, the algae model are established base on researching formation mechanism of water bloom, and the unknown parameters of the model are optimized with particle swarm optimization. The calculated value and real data are compared in the model, the results shows that the basic change of chlorophyll from production to outbreak of algae blooms, but not reflecte during the outbreak stage. Considering its own nonlinear characteristics of neural network model to compensate the shortage of the mechanism model, The simulation results verify the superiority outbreaks of algae blooms of the model, the forecast precision of the model is improved greatly. It supplies new approach for establishing model of combining mechanism and intelligence.Finally, summarize the research work of this thesis and put forward future development trend.The innovation of this paper has the following two points: (1)Constructing water quality parameters of weight allocation model by using the genetic algorithm to optimize the theoretical for lake and resevoir, combining the fuzzy comprehensive evaluation method to improve the effectiveness and objectivity of water quality evaluation for the lake and resevoir.(2)Determining the basic trend of algae blooms by Using algae model, Particle Swarm Optimization are used to optimize model parameters. The error of the existing model are nonlinear compensated by neural network, the prediction precision improves drmatically.