The Applications Of Particle Swarm Algorithm And Neural Network For Atmospheric Quality Evaluation And Forecast

1. An integrated PSO based model for the evaluation of air quality pollution.Applied the PSO method with the global searching capability, this paper has optimized the parameters in the generalized formula for air pollution loss rate calculation. Therefore the formula gets more generalizations, namely the integrated PSO based model. Numerical result of examples show that the proposed model has considerable feasibility and effectiveness, and may be applicable practice.1.1 The generalized formula for the calculation of air quality pollution. The air pollution loss rate of the ith pollutant could be expressed as: (1)where ai and bi are the parameters related to the ith pollutant which are required, and ci the density of the ith pollutant measured in practice. LiZuoyong substituted ci with the relative density, namely xi=ci / ci0, ci0 is a predefined parameter of the ith pollutant, usually take as the natural basic density of the ith pollutant. So Eq. (1) may be rewrited as: (2)To strengthen the generalization of the model, QianLianwen introduced an universal parameter c which is required and unrelated to the ith pollutant in Eq. (2). Then Eq. (2) could be rewrited as: (3) The optimization of parameters by PSO methodAn objective function is required when optimizing the parameters of Eq. (3) using PSO method, namely that: (4) where m=7 is the number of the selected pollutants; k=5 the number of the air pollution levels; Rik the air pollution loss rate of the ith pollutant for level k, and Rke the objective value of the air pollution loss rate for level k, respectively. In the process of the optimizing of the parameters of Eq. (4) by PSO method, we find that the values of parameter are changed obviously when scope of the parameters of PSO are selected differently. Moreover, the value of a is approached to the lower limit of the selected scope of PSO parameter. According to the above fact, the functional relation to explain the air pollution loss rate and the relative density of pollutants may be decided by two parameters mainly. So the best solution can be achieved by PSO method for Eq. (4), that is a=63.93 and b=0.3401. Then we get the equation as follows: (5) 1.3. The integrated PSO based model for the evaluation of air quality pollution The generalized equation with multi-pollutants for synthetical air quality pollution loss rate can be expressed as: (6)where m is the number of the kinds of pollutants, Ri the air pollution loss rate of the ith pollutant, wi the normalized weight value of the pollutant defined by the relative importance of the corresponding value of k, respectively. 1.4 The air quality pollution loss index model The air quality pollution loss index may be defined as: (7) 1.5 The integrated PSO based loss index model for the evaluation of air quality pollutionThe generalized index equation with multi-pollutants for synthetical air quality pollution loss rate can be expressed as: (8)where Ii is the air quality pollution loss index of the ith pollutant, wi the normalized weight value of the pollutant. 1.6 Numerical results Two examples are executed to test the efficiency of the proposed model in this thesis. For example 1, we take the practical density value of three pollutants, namely SO2, Nox and TSP of ten supervision spots in a city as the input data and evaluate the air quality by the proposed model. Numerical result shows that the model is quite efficient.For example 2, we take the practical density value of three pollutants, namely PM10, SO2 and NO2 in Changchun from Jan. 1 2002 to Mar. 31 2002 as the input data and evaluate the air quality by the proposed model. Numerical result has a better precision compared with the practical values.2. The application of OIF Elman neural network model for air pollution forecasting.Applied the OIF Elman(Output-Input Feedback Elman (OIF Elman)) neural network to foreca...