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Air Pollutant Concentration Forecast Based on Support Vector Regression and Quantum-Behaved Particle Swarm Optimization

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Abstract

In order to improve the forecasting accuracy of atmospheric pollutant concentration, a prediction model of atmospheric PM2.5 and nitrogen dioxide (NO2) concentration based on support vector regression (SVR) is established. Quantum-behaved particle swarm optimization (QPSO) algorithm is used to select the optimal parameters influencing the performance of SVR. And in order to improve the problem that the fixed SVR model is difficult to adapt to the highly nonlinear process, a simple online SVR based on re-modeling method is proposed instead of the fixed one. According to hourly PM2.5 and NO2 concentrations and meteorological conditions from May 2014 to April 2015 in Wanliu Monitoring Station of Beijing in China, the experiment is carried out based on the data of 3 months. Meanwhile, PM2.5 concentration is predicted by three different prediction methods, including the recursive prediction method, direct prediction method, and online direct prediction method. The results show that the online direct prediction method is the most accurate in the three prediction methods. In addition, compared with original particle swarm optimization (PSO) algorithm, QPSO algorithm is tested more efficiently for the improvement of global search ability and robustness during the procedure of parameter selection. Moreover, the hybrid QPSO-SVR model proposed in this paper has higher prediction accuracy and less computational time compared with the PSO-SVR model, genetic algorithm (GA)-SVR model, and grid search (GS)-SVR model, which indicates reliability and effectiveness of the QPSO-SVR model in prediction of these two pollutant concentrations.

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Abbreviations

AI:

Artificial intelligence

ARMA:

Autoregressive moving average

ANN:

Artificial neural network

SVM:

Support vector machine

SVR:

Support vector regression

PLS:

Partial least squares

PSO:

Particle swarm optimization

QPSO:

Quantum-behaved particle swarm optimization

GA:

Genetic algorithm

GS:

Grid search

PM:

Particulate matter

NO2 :

Nitrogen dioxide

CO:

Carbon monoxide

CO2 :

Carbon dioxide

SO2 :

Sulfur dioxide

CH4 :

Methane

NOx :

Nitrogen oxides

O3 :

Ozone

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Funding

This work is supported by the Fund of National Natural Science Foundation of China (61873006, 61473034 and 61673053), Beijing Nova Programme Interdisciplinary Cooperation Project (Z161100004916041) and Project of Beijing science and technology commission: Research on key technologies of intelligent factory interconnection and industrial Internet of things equipment.

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Xiaoli Li, Aorong Luo & Jiangeng Li

  2. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China

    Xiaoli Li, Aorong Luo & Jiangeng Li

  3. Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China

    Xiaoli Li

  4. School of International Studies, Communication University of China (CUC), Beijing, 100024, China

    Yang Li

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  1. Xiaoli Li
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  2. Aorong Luo
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Correspondence to Jiangeng Li.

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Li, X., Luo, A., Li, J. et al. Air Pollutant Concentration Forecast Based on Support Vector Regression and Quantum-Behaved Particle Swarm Optimization. Environ Model Assess 24, 205–222 (2019). https://doi.org/10.1007/s10666-018-9633-3

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