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Predicting the Effect of the COVID-19 Pandemic on Air Pollution in Amman, Jordan Using an Artificial Neural Network Model


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DOI: https://doi.org/10.15866/iree.v18i2.22136

Abstract


This study aims to see how well three different types of Artificial Neural Networks (ANNs) can predict the concentrations of four air pollutants (CO, NO2, PM10, and SO2) before, during, and after the COVID-19 pandemic. MATLAB software was used to construct and test the suggested networks. The World Air Quality Project website included the metrological data and data on air pollutant concentrations. The data were utilized to approximate and estimate the actual performance of proposed models during the development phase. The ANN findings were validated using the results generated during the training phase. Statistics on the three metrology variables were used to compare the performance of the three models (R, RMSE, and MBE). The Elman model was the best and most accurate coefficient of correlation (R) and provided the more precise correlation between meteorological factors and air pollution concentrations in Amman, Jordan.
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Keywords


COVID-19; Coronavirus; Weather Data; Artificial Neural Network

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References


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