Seasonal prediction of PM2.5 based on support vector machine model and multiple regression model

Shuran Yang

doi:10.1117/12.2626433

21 December 2021 Seasonal prediction of PM2.5 based on support vector machine model and multiple regression model

Shuran Yang

Proceedings Volume 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021); 1215619 (2021) https://doi.org/10.1117/12.2626433
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 2021, Sanya, China

Abstract

Air pollution, an issue that requires world-wide attention, has been a long-lasting problem, especially in north Asia. PM2.5 concentration that exceeds the standard value has been a main cause to people’s both mental and physical health. As the environment conservation is on going, prediction models need persistent emphasis to better forecast air quality with great accuracy so that citizens can better manage life schedule and impact of air pollution can be alleviated. This paper chose Xuhui District, Shanghai as the prediction area, collected year-round data accurate to hour, chose data from numerous dimensions which covers gaseous influents and meteorological factors, and set these input values with different weights when training data based on the degree these factors lead to air quality fluctuations. Besides, the research integrated polynomial regression model and Support Vector Machine models, which are two methods with great difference, so as to compensate for each other’s prediction of PM2.5 concentration disadvantages. when combining these two models and setting different weights to the results of these two models, the new result of predicted PM2.5 is closer to the real value.

Citation Download Citation

Shuran Yang "Seasonal prediction of PM2.5 based on support vector machine model and multiple regression model", Proc. SPIE 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 1215619 (21 December 2021); https://doi.org/10.1117/12.2626433

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