Evaluation of Sulphur Dioxide Hourly Prediction Using Long Short-term Memory for Summer and Winter Season

Rocznik Ochrona Środowiska 2024, vol. 26, pp. 313-321

Mohammed Bennis¹, Youssfi Mohamed¹ , Rachida El Morabet¹ , Majed Alsubih² , Muneer Prayanagat², Roohul Abad Khan² This email address is being protected from spambots. You need JavaScript enabled to view it.

	1. University Hassan II, Casablanca, Morocco 2. King Khalid University, Abha, Saudi Arabia
	This email address is being protected from spambots. You need JavaScript enabled to view it.
	https://doi.org/10.54740/ros.2024.031

Increasing air pollution has necessitated the prediction of pollutants over time. Deterministic, statistical, and machine-learning methods have been adopted to predict and forecast pollutant levels. It aids in planning and adopting measures to overcome the adverse effects of air pollution. This study employs long short-term memory (LSTM). This study used the hourly data from a meteorological station in a low-town area, Mohammedia City, Morocco. The model prediction accuracy was evaluated based on hourly, weekly, and seasonal (summer and winter) readings for the summer and winter of 2019, 2020 and 2021. Root mean square error (RMSE), mean absolute error (MAE) and mean arctangent absolute percentage error (MAAPE) were calculated to evaluate the accuracy of the developed LSTM model. The MAE value of 0.026 was observed to be less in winter than 0.029 during summer in 2019. Also, it was observed that MAE values decreased from Year 2019-2021, indicating increased prediction accuracy. MAAPE also observed a similar trend. However, RMSE values indicated the opposite for 2019 and 2020; in 2021, the RMSE value was 0.21 for summer and 0.14 for winter for hourly readings. Based on the error calculation, the study found weekly hourly readings were the most accurate for predicting SO₂ concentration. Also, the LSTM model was more accurate in predicting winter SO₂ concentration than in the summer season. Further studies must incorporate local incidences affecting the SO₂ concentration into the LSTM model to increase its accuracy.

sulphur dioxide, machine learning, long short-term memory, mean absolute error, root mean square error

AMA Style
Bennis M, Mohamed Y, El Morabet R, Alsubih M, Prayanagat M, Khan R. Evaluation of Sulphur Dioxide Hourly Prediction Using Long Short-term Memory for Summer and Winter Season. Rocznik Ochrona Środowiska. 2024; 26. https://doi.org/10.54740/ros.2024.031

ACM Style
Bennis, M., Mohamed, Y., El Morabet, R., Alsubih, M., Prayanagat, M., Khan, R. 2024. Evaluation of Sulphur Dioxide Hourly Prediction Using Long Short-term Memory for Summer and Winter Season. Rocznik Ochrona Środowiska. 26. DOI:https://doi.org/10.54740/ros.2024.031

ACS Style
Bennis, M.; Mohamed, Y.; El Morabet, R.; Alsubih, M.; Prayanagat, M.; Khan, R. Evaluation of Sulphur Dioxide Hourly Prediction Using Long Short-term Memory for Summer and Winter Season Rocznik Ochrona Środowiska 2024, 26, 313-321. https://doi.org/10.54740/ros.2024.031

APA Style
Bennis, M., Mohamed, Y., El Morabet, R., Alsubih, M., Prayanagat, M., Khan, R. (2024). Evaluation of Sulphur Dioxide Hourly Prediction Using Long Short-term Memory for Summer and Winter Season. Rocznik Ochrona Środowiska, 26, 313-321. https://doi.org/10.54740/ros.2024.031

ABNT Style
BENNIS, M.; MOHAMED, Y.; EL MORABET, R.; ALSUBIH, M.; PRAYANAGAT, M.; KHAN, R. Evaluation of Sulphur Dioxide Hourly Prediction Using Long Short-term Memory for Summer and Winter Season. Rocznik Ochrona Środowiska, v. 26, p. 313-321, 2024. https://doi.org/10.54740/ros.2024.031

Chicago Style
Bennis, Mohammed, Mohamed, Youssfi, El Morabet, Rachida, Alsubih, Majed, Prayanagat, Muneer, Khan, Roohul_Abad. 2024. "Evaluation of Sulphur Dioxide Hourly Prediction Using Long Short-term Memory for Summer and Winter Season". Rocznik Ochrona Środowiska 26, 313-321. https://doi.org/10.54740/ros.2024.031

Harvard Style
Bennis, M., Mohamed, Y., El Morabet, R., Alsubih, M., Prayanagat, M., Khan, R. (2024) "Evaluation of Sulphur Dioxide Hourly Prediction Using Long Short-term Memory for Summer and Winter Season", Rocznik Ochrona Środowiska, 26, pp. 313-321. doi:https://doi.org/10.54740/ros.2024.031

IEEE Style
M. Bennis, Y. Mohamed, R. El Morabet, M. Alsubih, M. Prayanagat, R. Khan, "Evaluation of Sulphur Dioxide Hourly Prediction Using Long Short-term Memory for Summer and Winter Season", RoczOchrSrod, vol 26, pp. 313-321. https://doi.org/10.54740/ros.2024.031