@Article{cmc.2022.021629,
AUTHOR = {Muhammad Zulqarnain, Rozaida Ghazali, Habib Shah, Lokman Hakim Ismail, Abdullah Alsheddy, Maqsood Mahmud},
TITLE = {A Deep Two-State Gated Recurrent Unit for Particulate Matter (PM<sub>2.5</sub>) Concentration Forecasting},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {71},
YEAR = {2022},
NUMBER = {2},
PAGES = {3051--3068},
URL = {http://www.techscience.com/cmc/v71n2/45792},
ISSN = {1546-2226},
ABSTRACT = {Air pollution is a significant problem in modern societies since it has a serious impact on human health and the environment. Particulate Matter (PM<sub>2.5</sub>) is a type of air pollution that contains of interrupted elements with a diameter less than or equal to 2.5 m. For risk assessment and epidemiological investigations, a better knowledge of the spatiotemporal variation of PM<sub>2.5</sub> concentration in a constant space-time area is essential. Conventional spatiotemporal interpolation approaches commonly relying on robust presumption by limiting interpolation algorithms to those with explicit and basic mathematical expression, ignoring a plethora of hidden but crucial manipulating aspects. Many advanced deep learning approaches have been proposed to forecast Particulate Matter (PM<sub>2.5</sub>). Recurrent neural network (RNN) is one of the popular deep learning architectures which is widely employed in PM<sub>2.5</sub> concentration forecasting. In this research, we proposed a Two-State Gated Recurrent Unit (TS-GRU) for monitoring and estimating the PM<sub>2.5</sub> concentration forecasting system. The proposed algorithm is capable of considering both spatial and temporal hidden affecting elements spontaneously. We tested our model using data from daily PM<sub>2.5</sub> dimensions taken in the contactual southeast area of the United States in 2009. In the studies, three evaluation matrices were utilized to compare the overall performance of each algorithm: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The experimental results revealed that our proposed TS-GRU model outperformed compared to the other deep learning approaches in terms of forecasting performance.},
DOI = {10.32604/cmc.2022.021629}
}