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A Deep Two-State Gated Recurrent Unit for Particulate Matter (PM_(2.5)) Concentration Forecasting

Computers, Materials & Continua 2022年第5期71卷 3051-3068页

作者： Muhammad Zulqarnain Rozaida Ghazali Habib Shah Lokman Hakim Ismail abdullah alsheddy Maqsood Mahmud Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaBatu PahatJohorMalaysia College of Computer Science King Khalid UniversityAbhaSaudi Arabia College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic UniversityRiyadhSaudi Arabia Department of Management Information Systems College of Business AdministrationImam Abdulrahman Bin Faisal University31441DammamSaudi Arabia

Air pollution is a significant problem in modern societies since it has a serious impact on human health and the *** 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 *** risk assessment and epidemiological investigations,a better knowledge of the spatiotemporal variation of PM_(2.5) concentration in a constant space-time area is *** 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 *** 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 *** this research,we proposed a Two-State Gated Recurrent Unit(TS-GRU)for monitoring and estimating the PM_(2.5) concentration forecasting *** proposed algorithm is capable of considering both spatial and temporal hidden affecting elements *** tested our model using data from daily PM_(2.5) dimensions taken in the contactual southeast area of the United States 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.

关键词： Deep learning PM_(2.5)forecasting air pollution two-state GRU

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