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A decomposition-ensemble broad learning system for AQI forecasting

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Abstract

The combination of decomposition algorithms and prediction models is a common structure for air quality time series forecasting. In this study, we develop a dynamic decomposition framework by adding the time window based on empirical mode decomposition, ensemble empirical mode decomposition and complementary ensemble empirical mode decomposition with adaptive noise. Moreover, we proposed a decomposition-ensemble broad learning system for air quality index forecasting based on broad learning system (BLS) which is a simple and efficient neural network. In this model, we combine the developed decomposition algorithms with BLS to bring out their merits. The air pollution datasets from Huainan city and Fuyang city are utilized to establish the empirical experiment. The experimental results reveal that our proposed ensemble model outperforms other baselines, including prediction models and models with decomposition algorithms.

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Acknowledgements

This work is partially supported by the National Natural Science Foundation of China under Grants 72004174 and Natural Science Foundation of Guangdong Province, China (2020A1515010761).

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Authors and Affiliations

  1. School of Computer, South China Normal University, Guangzhou, Guangdong, China

    Choujun Zhan

  2. School of Electrical and Computer Engineering, Nanfang College Guangzhou, Guangzhou, Guangdong, China

    Choujun Zhan, Wei Jiang, Fabing Lin & Shuntao Zhang

  3. School of Economics, Wuhan University of Technology, Wuhan, China

    Bing Li

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  1. Choujun Zhan
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  2. Wei Jiang
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  3. Fabing Lin
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  4. Shuntao Zhang
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  5. Bing Li
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Correspondence to Bing Li.

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Zhan, C., Jiang, W., Lin, F. et al. A decomposition-ensemble broad learning system for AQI forecasting. Neural Comput & Applic 34, 18461–18472 (2022). https://doi.org/10.1007/s00521-022-07448-2

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  • DOI: https://doi.org/10.1007/s00521-022-07448-2

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