Method for dynamically fusing, counting and forecasting air quality based on dynamic and thermal factors

Abstract

The invention discloses a method for dynamically fusing, counting and forecasting air quality based on dynamic and thermal factors. The method comprises the following steps of: collecting data; introducing dynamic and thermal influence factors; performing empirical orthogonal decomposition of a vector matrix composed of influence factors having the significance level alpha, which is equal to 0.01, and selecting a principal component, the cumulative variance contribution of which is beyond 98%; establishing a regression equation by using the principal component; establishing a neural network model by utilizing a back-propagation neural network algorithm; performing evaluation check of a fitting result of the regression equation and the neural network model and the historical forecasting accuracy; calculating the final fusing and forecasting result by using a weighted average algorithm; performing evaluation check of the accuracy of the final fusing and forecasting result; and, adding new data into a historical data set in real time, and dynamically updating a forecasting model according to a check evaluation result. Compared with the existing method, the method disclosed by the invention has the advantages that: the relative error of various pollutant concentration forecasts is reduced by 3-11%; and the level forecasting accuracy rate is increased by 4-8%.

Description

technical field [0001] The invention relates to the technical field of air quality statistical forecasting, in particular to an air quality dynamic fusion statistical forecasting method based on dynamic and thermal factors. Background technique [0002] Ambient air quality statistical forecasting is based on statistical methods, using existing data, based on statistical analysis, to study the changing law of the atmospheric environment, to establish a statistical forecasting model between the concentration of air pollution and meteorological parameters, and to predict the concentration of air pollutants. Currently widely used statistical forecasting methods include weather situation classification method, regression equation method, artificial neural network method, etc., but the three main statistical forecasting methods have certain limitations. [0003] The weather situation classification method determines the high-concentration weather situation and the low-concentratio...

AI Technical Summary

Problems solved by technology

[0008] Aiming at the shortcomings and deficiencies of existing statistical forecasting methods, the present invention provides a dynamic fusion statistical forecasting method for ambient air quality based on atmospheric dynamic and thermal factors, and selects several principal components that contribute the most to factor orthogonal decomposition

[0009] Aiming at the shortcomings of selecting a large number of basic meteorological elements as statistical forecasting fact

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