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Hybrid learning model for spatio-temporal forecasting of PM\(_{2.5}\) using aerosol optical depth

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Abstract

Existence of several challenges and high cost in the development of monitoring infrastructure have become major reasons for data sparsity by statutory government agencies tasked to study pollution exposure in urban areas. As an effort to mitigate this problem, the recent usage of satellite aerosol optical depth data along with the usage of learning algorithms have become popular in recent times. This paper presents a novel four-staged approach using different machine learning, deep learning and statistical methods to develop a spatio-temporal hybrid model for temporal forecasting using data from existing stations along with satellite aerosol optical depth data for spatial interpolation. Experiments conducted on real-world data belonging to the cities of Kolkata, Bengaluru and Mumbai show that a consistent pattern is not followed in all the cities in all stages except in spatial interpolation where Random Forest Regression is found to surpass all other models used. While a long short-term memory network (LSTM Auto-Encoder) when employed in temporal forecasting inside the hybrid method outperforms others in Mumbai, a random forest regression-based method and a multi-layer perceptron-based method outperform others similarly in Kolkata and Bengaluru, respectively.

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Data and Code Availability

The analysis code and data used for this paper upon publication can be found in the following link - https://github.com/nathzi1505/AOD-Hybrid-Paper.

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Acknowledgements

The research work of Asif Iqbal Middya is partially supported by UGC-NET Junior Research Fellowship (UGC-Ref. No.:3684 / (NET-JULY 2018)) provided by the University Grants Commission, Government of India. This research work is also supported by the project entitled “Participatory and Realtime Pollution Monitoring System For Smart City, funded by Higher Education, Science & Technology and Biotechnology, Department of Science & Technology, Government of West Bengal, India”.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Pritthijit Nath, Asif Iqbal Middya & Sarbani Roy

  2. Department of Instrumentation and Electronics Engineering, Jadavpur University, Kolkata, India

    Biparnak Roy

  3. Department of Computer Science, SRM University, Kattankulathur, Chennai, India

    Pratik Saha

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Nath, P., Roy, B., Saha, P. et al. Hybrid learning model for spatio-temporal forecasting of PM\(_{2.5}\) using aerosol optical depth. Neural Comput & Applic 34, 21367–21386 (2022). https://doi.org/10.1007/s00521-022-07616-4

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