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Comparison of hybrid machine learning model for the analysis of black carbon in air around the major coal mines of India

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Abstract

Air is the basis for the existence of life on Earth; but in the present age of modernization the degrading quality of air year by year, due to the growth of Industrialization, urbanization, automobiles, coal-fired thermal power plants, and various other factories is the matter of real concern. To predict the future growth of air pollutants numerous prediction models have been developed by researchers. Time-series ARIMA model although quite useful for forecasting but fails to handle non-stationary problems. Among all the existing forecasting models, wavelets along with the Machine learning models have proved to be very successful and have been widely used in various fields like mathematical modeling, signal recognition, image recognition, classification, function approximation, data processing, filtering, clustering, compression, robotics, and decision making. It is also used in the field of mathematical forecasting for developing efficient prediction models. This paper aims to develop a wavelet-ANFIS conjugation model and a wavelet-ARIMA coupled model along with the time-series ARIMA model for the prediction of black carbon concentration over the Raniganj, Jharia, and Bokaro coal mines of India, by considering a long term data obtained by NASA (http://nasa.gov/) and compare the results obtained by these models for determining the best prediction model. The validity of the results is tested with the help of error measures like RMSE, MSE, MAPE, MAE, and relative error. Results over the three sample sites conclude that the Wavelet-ANFIS conjugation approach outperforms the wavelet-ARIMA coupled approach and the simple time-series ARIMA model.

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Acknowledgements

The authors are thankful to CSIR-National Physical Laboratory, New Delhi for providing the data for research. We are also grateful to Lovely Professional University Punjab, I. K. Gujral, Punjab Technical University Jalandhar, Sri Guru Angad Dev College, Khadoor Sahib, Tarn Taran for providing facilities for research work. The corresponding author also thankful to SERB-DST, Government of India for the financial support with the research project MATRICS MTR/2020/000479.

Funding

The corresponding author received the funding from SERB-DST government of India under the MATRICS project (MTR/2020/000479).

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Authors and Affiliations

  1. Department of Mathematics, Sri Guru Angad Dev College, Khadoor Sahib, Tarn Taran, Punjab, 143117, India

    Sidhu Jitendra Singh Makkhan

  2. Department of Mathematics, Guru Nanak Dev University College, Narot Jaimal Singh, Pathankot, Punjab, 145026, India

    Sarbjit Singh

  3. Department of Mathematical Sciences, I. K. Gujral Punjab Technical University, Jalandhar, Punjab, 144603, India

    Kulwinder Singh Parmar

  4. Department of Mathematics, School of Chemical Engineering and Physical Sciences, Lovely Professional University, Phagwara, Punjab, 144411, India

    Sachin Kaushal

  5. CSIR-National Physical Laboratory, New Delhi, 110012, India

    Kirti Soni

  6. Department of Mathematics, Guru Nanak Dev University, Amritsar, Punjab, 143005, India

    Sarbjit Singh

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Makkhan, S.J.S., Singh, S., Parmar, K.S. et al. Comparison of hybrid machine learning model for the analysis of black carbon in air around the major coal mines of India. Neural Comput & Applic 35, 3449–3468 (2023). https://doi.org/10.1007/s00521-022-07909-8

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