Abstract
Abstract The rapid economic development has led to the declining quality of the atmospheric environment. At present, my country is facing a very serious problem of atmospheric environmental pollution. Accurate prediction of air quality plays a vital role in the realization of air pollution control by environmental protection departments. Based on the historical air pollution concentration data, this paper establishes a BP neural network model to learn the statistical law of air pollutant values to realize the prediction of air quality in the future. Through the analysis of the target of air quality prediction, the design of an air quality prediction method based on BP neural network is designed. This method includes four stages: air pollutant concentration data collection, data processing, air quality index calculation, and prediction network construction. The experimental results show that the air quality prediction method based on BP neural network designed and implemented in this paper, combined with the developed air quality prediction system, can effectively predict the recent changes in air quality and various air pollutant concentrations. By collecting the concentration data of air pollutants and learning the changes of air pollutants to achieve air quality prediction, it provides a quantitative reference for government environmental protection departments to achieve air pollution control.
Highlights
Air quality prediction, as the name suggests, is based on the historical emission concentration values of various pollutant items in the air to predict the concentration values of various pollutants in the air pollution in the future and the air environment quality[1]
As China's rapid economic development has led to serious atmospheric environmental pollution problems, the state and the public have paid more and more attention to the treatment and prevention of air pollution
This paper studies the air quality prediction method, and the construction of the air quality prediction model mainly needs to consider the lack of data Processing, data outlier processing, and data normalization processing
Summary
As the name suggests, is based on the historical emission concentration values of various pollutant items in the air to predict the concentration values of various pollutants in the air pollution in the future and the air environment quality[1]. The environmental protection departments of local governments strengthen air pollution control work, hoping to understand the changes in air quality in a timely manner by establishing an air quality prediction model. Due to its own characteristics, numerical prediction requires detailed geographic, meteorological, and pollution sources to realize the air quality prediction process. Collecting these data in actual situations requires huge costs and is difficult to obtain. This paper uses air quality prediction based on BP neural network, and builds a neural network model to achieve air quality prediction, providing government environmental protection departments with air pollution trends
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