TY - EJOU
AU - Zulqarnain, Muhammad
AU - Ghazali, Rozaida
AU - Shah, Habib
AU - Ismail, Lokman Hakim
AU - Alsheddy, Abdullah
AU - Mahmud, Maqsood
TI - A Deep Two-State Gated Recurrent Unit for Particulate Matter (PM_{2.5}) Concentration Forecasting
T2 - Computers, Materials \& Continua
PY - 2022
VL - 71
IS - 2
SN - 1546-2226
AB - Air pollution is a significant problem in modern societies since it has a serious impact on human health and the environment. Particulate Matter (PM_{2.5}) 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_{2.5} 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_{2.5}). Recurrent neural network (RNN) is one of the popular deep learning architectures which is widely employed in PM_{2.5} concentration forecasting. In this research, we proposed a Two-State Gated Recurrent Unit (TS-GRU) for monitoring and estimating the PM_{2.5} 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_{2.5} 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.
KW - Deep learning; PM_{2.5} forecasting; air pollution; two-state GRU
DO - 10.32604/cmc.2022.021629