Modified Logistic-RBF Combination Model Construction And Its Application In Air Quality Assessment

The report of the 19 th National Congress emphasized that it is necessary to continue the air pollution prevention and control actions,win the blue sky defense war,and strive to make the people have more sense of happiness and happiness in the beautiful ecological environment.In this background,the effective assessment of the quality of the air environment is a positive response to air pollution control actions,and is also an important criterion for testing the effectiveness of air pollution prevention and control.Therefore,high-precision evaluation of air quality under the economic high-quality development pattern has important guiding significance.In order to achieve high-precision evaluation of air quality,and considering the advantages and disadvantages of the model and the feasibility of solving the weight of the combined model.Logistic regression models with good robustness in statistics and RBF neural network models with high training speed and accuracy in non-statistics were selected.Firstly,the air quality standard level is divided into five levels according to the air quality standard level and the actual situation of the air environmental pollutant concentration value in Jiangxi Province,and 100 sets of data are randomly generated within five air quality standard levels to meet the requirements of model training.At the same time,the factor analysis method is used to select the pollutant index to ensure that the selected key pollutant index has the most significant impact on the final evaluation result in the category,which can reduce the impact of the low concentration value pollutant on the final evaluation result,and also avoid The subjectivity of artificially selected indicators.Then,by studying the range and distribution trend of air quality pollutant concentration values under actual conditions,the logistic regression model is proposed and modified.The genetic algorithm in the software 1st Opt is used to optimize the logistic regression model parameters,and finally the modified logistic regression model is obtained and passed.The fitting model test and the test of the model parameters were carried out.Secondly,in the MATLAB,the RBF neural network model was trained with 100 sets of data to achieve the accuracy requirement and then converge.Finally,the linear programming method is used to combine the two single models.The combined model compares the evaluation results of the combined model with the evaluation results of the single model through the validity and correlation test.It is found that the combined model is superior in robustness and precision.Based on this,a modified Logistic-RBF network combination model is proposed.An example analysis of air quality in Jiangxi Province was conducted and compared with the results of a single model and conventional evaluation methods.The results show that the evaluation results obtained by the combined model are strikingly consistent with the evaluation results of the conventional methods,indicating the applicability of the combined model.The accuracy and robustness of the combined model are higher than the single model and the evaluation results are continuous.The real value solves the incompatibility problem when the conventional evaluation method evaluates the air quality,that is,the area within the same level obtained by the traditional evaluation method can be compared.Therefore,this paper proposes a new air quality assessment method with wide application value.