پیش بینی غلظت آلاینده های اتمسفری
Abstract
1. Introduction
2. Data collecting and pre-processing
3. Influential factors analysis for SAPCF
4. Architecture of the proposed hybrid PSO–SVM model based on K-means
5. Case study and forecasting result analysis
6. Discussions
7. Conclusion
Acknowledgements
References
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.
Introduction
As one of the four elements of life, air plays a major role in maintaining the ecosystem. However, human activities have seriously aggravated the degree of air pollution, thereby prompting researchers to conduct pollution analysis (Bollen, 2015) and predict pollutant concentrations (Wu et al., 2018). Given its important role in formulating effective precautionary measures, atmospheric pollutant concentration forecasting (APCF) has received much research attention (Bai et al., 2018). Previous studies have classified APCF into short-term APCF (SAPCF) (Niu et al., 2016), medium and long term APCF. Medium and long term APCF forecasts the concentration of pollutants over a relatively long period, usually ranging from months to years (Nebenzal and Fishbain, 2018), and is mainly used for planning the distribution of industrial sites or residential areas. Meanwhile, SAPCF reveals the vital status in many basic operations and is often used in planning abatement actions and transportation networks in advance (Li and Tao, 2018; Xie et al., 2018; Zhai and Chen, 2018). SAPCF can also help governments save time in responding to pollution-related problems (Y.F. Wang et al., 2018) and help individuals prevent exposure to pollutants (Soh et al., 2018). Therefore, an accurate SAPCF is of great significance at the social and individual levels. Researchers have proposed numerous methods for SAPCF in recent years as will be discussed in Section 1.1.