A hybrid PSO-SVM model based on clustering algorithm for short-term atmospheric pollutant concentration forecasting

Shuixia Chen, Jian-qiang Wang and Hong-yu Zhang

Technological Forecasting and Social Change, 2019, vol. 146, issue C, 41-54

Abstract: Air pollution can lead to a wide range of hazards and can affect most organisms on Earth. Therefore, managing and controlling air pollution has become a top priority for many countries. An effective short-term atmospheric pollutant concentration forecasting (SAPCF) can mitigate the negative effects of atmospheric pollution. In this paper, we propose a new hybrid forecasting model for SAPCF. Firstly, we analyse the influential factors of pollutants to obtain the optimal combination of input variables. Secondly, we use a clustering algorithm to enhance the regularity of our modelling data. Thirdly, we build a particle swarm optimisation (PSO)–support vector machine (SVM) hybrid model called PSO–SVM and perform a case study in Temple of Heaven, Beijing to test its forecasting accuracy and validate its performance against three contrastive models. The first model inputs all possible variables in equal weight without influence factor analysis. The second model integrates the same input variables used in the proposed model without clustering. The third model inputs these same variables with genetic-algorithm optimised SVM parameters. The comparison amongst these models demonstrates the superior performance of our proposed hybrid model. We further verify the forecasting results of our hybrid model by conducting statistical tests.

Keywords: Short-term atmospheric pollutant concentration forecasting; Influential factors analysis; Clustering algorithm; Particle swarm optimisation; Support vector machine (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:146:y:2019:i:c:p:41-54

DOI: 10.1016/j.techfore.2019.05.015

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