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Application of a Hybrid Model Based on Echo State Network and Improved Particle Swarm Optimization in PM 2.5 Concentration Forecasting: A Case Study of Beijing, China

Author

Listed:
  • Xinghan Xu

    (Department of Environmental Engineering, Kyoto University, Kyoto 615-8540, Japan)

  • Weijie Ren

    (Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China)

Abstract

With the acceleration of urbanization, there is an increasing trend of heavy pollution. PM 2.5 , also known as fine particulate matter, refers to particles in the atmosphere with a diameter of less than or equal to 2.5 microns. PM 2.5 has a serious impact on human life, a sustainable city, national economic development, and so on. How to forecast the PM 2.5 concentration accurately, and then formulate a scientific air pollution prevention and monitoring program is of great significance. This paper proposes a hybrid model based on echo state network (ESN) and an improved particle swarm optimization (IPSO) algorithm for the Beijing air pollution problem, and provides a method for PM 2.5 concentration forecasting. Firstly, the PSO algorithm is improved to speed up the search performance. Secondly, the optimal subset of the original data is selected by the convergence cross-mapping (CCM) method. Thirdly, the phase space reconstruction (PSR) process is combined with the forecasting model, and some parameters are optimized by the IPSO. Finally, the optimal variable subset is used to predict PM 2.5 concentration. The 11-dimensional air quality data in Beijing from January 1 to December 31, 2016 are analyzed by the proposed method. The experimental results show that the hybrid method is superior to other comparative models in several evaluation indicators, both in one-step and multi-step forecasting of PM 2.5 time series. The hybrid model has good application prospects in air quality forecasting and monitoring.

Suggested Citation

  • Xinghan Xu & Weijie Ren, 2019. "Application of a Hybrid Model Based on Echo State Network and Improved Particle Swarm Optimization in PM 2.5 Concentration Forecasting: A Case Study of Beijing, China," Sustainability, MDPI, vol. 11(11), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:11:p:3096-:d:236254
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    References listed on IDEAS

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    1. Guanghui Yuan & Weixin Yang, 2019. "Evaluating China’s Air Pollution Control Policy with Extended AQI Indicator System: Example of the Beijing-Tianjin-Hebei Region," Sustainability, MDPI, vol. 11(3), pages 1-21, February.
    2. Fang Wang & Yaoyao Peng & Chunyan Jiang, 2017. "Influence of Road Patterns on PM 2.5 Concentrations and the Available Solutions: The Case of Beijing City, China," Sustainability, MDPI, vol. 9(2), pages 1-17, February.
    3. Qiongjie Dai & Jicheng Liu & Qiushuang Wei, 2019. "Optimal Photovoltaic/Battery Energy Storage/Electric Vehicle Charging Station Design Based on Multi-Agent Particle Swarm Optimization Algorithm," Sustainability, MDPI, vol. 11(7), pages 1-21, April.
    4. Wenyang Huang & Huiwen Wang & Yigang Wei, 2018. "Endogenous or Exogenous? Examining Trans-Boundary Air Pollution by Using the Air Quality Index (AQI): A Case Study of 30 Provinces and Autonomous Regions in China," Sustainability, MDPI, vol. 10(11), pages 1-20, November.
    5. Jianzhou Wang & Chunying Wu & Tong Niu, 2019. "A Novel System for Wind Speed Forecasting Based on Multi-Objective Optimization and Echo State Network," Sustainability, MDPI, vol. 11(2), pages 1-34, January.
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    2. Kayoung Kim & Young Ho Byun & Donghyuk Lee & Noeon Park, 2019. "Understanding the Global Status of Particulate Matter with Respect to Research Topics and Research Networks," Sustainability, MDPI, vol. 11(20), pages 1-16, October.
    3. Junbeom Park & Seongju Chang, 2021. "A Particulate Matter Concentration Prediction Model Based on Long Short-Term Memory and an Artificial Neural Network," IJERPH, MDPI, vol. 18(13), pages 1-15, June.
    4. Xue-Bo Jin & Xing-Hong Yu & Xiao-Yi Wang & Yu-Ting Bai & Ting-Li Su & Jian-Lei Kong, 2020. "Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System," Sustainability, MDPI, vol. 12(4), pages 1-18, February.
    5. Simona Tondelli & Ebrahim Farhadi & Bahareh Akbari Monfared & Mehdi Ataeian & Hossein Tahmasebi Moghaddam & Marco Dettori & Lucia Saganeiti & Beniamino Murgante, 2022. "Air Quality and Environmental Effects Due to COVID-19 in Tehran, Iran: Lessons for Sustainability," Sustainability, MDPI, vol. 14(22), pages 1-28, November.

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