Abstract
BP neural network is optimized by improved drosophila algorithm, and a prediction model for air quality in Nanchang is established based on the air quality data and meteorological data of Nanchang city in recent three years. The experimental results show that the improved algorithm has improved performance compared with the BP algorithm, and has improved accuracy 4%, with a small difference in time consumption. The performance of the indirect prediction method is slightly better than that of the direct prediction method
Highlights
In the 20th century, when air pollution events were concentrated in western countries, they began to predict the concentration of pollutants in the air
This paper aims to optimize and improve the prediction model of the BP neural network by using the fruit fly optimization algorithm and the chaos algorithm, and establish a prediction model with more accurate prediction results and higher prediction accuracy for air quality, and to provide people with more accurate travel Suggest
Based on the air quality index (AQI) data and weather monitoring data of Nanchang city from January 1, 2017 to December 31, 2019, this paper proposes a prediction model based on the BP neural network improved by drosophila optimization algorithm to predict AQI
Summary
In the 20th century, when air pollution events were concentrated in western countries, they began to predict the concentration of pollutants in the air. There have been many models, such as CMAQ (Community Multiscale Air Quality) model System[1,2], and ADMS (Atmospheric Dispersion Modelling System) series System[3,4]. In addition to these models, AERMOD model[5,6], CALPUFF model[7], and WRF-CHEM model[8,9] are commonly used air quality models. BP neural network has been widely used in the field of air pollution index prediction[10]. This paper aims to optimize and improve the prediction model of the BP neural network by using the fruit fly optimization algorithm and the chaos algorithm, and establish a prediction model with more accurate prediction results and higher prediction accuracy for air quality, and to provide people with more accurate travel Suggest
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