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

• Autorzy: Bennis Mohammed , Mohamed Youssfi , El Morabet Rachida , Alsubih Majed , Prayanagat Muneer , Khan Roohul Abad

Identyfikatory

DOI

10.54740/ros.2024.031

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

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

• Wydawca: Politechnika Koszalińska
• Czasopismo: Rocznik Ochrona Środowiska
• Rocznik: 2024
• Tom: Tom 26
• Strony: 313--321
• Opis fizyczny: Bibliogr. 16 poz., rys., tab.

Twórcy

autor

Bennis Mohammed

• 21ACS laboratory, ENSET Mohammedia, University Hassan II Casablanca, Morocco

autor

Mohamed Youssfi

• 21ACS laboratory, ENSET Mohammedia, University Hassan II Casablanca, Morocco

• https://orcid.org/0000-0003-2842-9880

autor

El Morabet Rachida

• LADES lab, FLSH-M, Hassan II University of Casablanca, Mohammedia, Morocco

• https://orcid.org/0000-0003-4750-644X

autor

Alsubih Majed

• Department of Civil Engineering, King Khalid University, Abha, Saudi Arabia

• https://orcid.org/0000-0001-8226-1682

autor

Prayanagat Muneer

• Department of Electrical Engineering, King Khalid University, Abha, Saudi Arabia

autor

Khan Roohul Abad

• Department of Civil Engineering, King Khalid University, Abha, Saudi Arabia

• https://orcid.org/0000-0002-2329-4123

Bibliografia

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