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Forecasting of fine particulate matter based on LSTM and optimization algorithm

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dc.citedby16
dc.contributor.authorZaini N.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorEan L.W.en_US
dc.contributor.authorChow M.F.en_US
dc.contributor.authorMalek M.A.en_US
dc.contributor.authorid56905328500en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid55324334700en_US
dc.contributor.authorid57214146115en_US
dc.contributor.authorid55636320055en_US
dc.date.accessioned2024-10-14T03:17:24Z
dc.date.available2024-10-14T03:17:24Z
dc.date.issued2023
dc.description.abstractAccurate air pollution forecasting may provide valuable information for urban planning to maintain environmental sustainability and reduce mortality risk due to health problems. The city with higher industrial activities, traffic congestion, population density, and energy consumption is most likely to produce higher pollution than the other cities. Therefore, this study uses hybrid deep learning models to forecast air pollution based on the concentration of particulate matter with diameter size of less than 2.5 ?m (PM2.5) for two air quality monitoring stations in Kuala Lumpur, Malaysia. The proposed models predict the hourly air pollutant concentration based on 4-h historical input based on six air pollutant data, meteorology parameters, and PM2.5 concentration data from the neighboring air quality monitoring stations. Long short-term memory based on metaheuristic algorithms, namely particle swarm optimization and sparrow search algorithm (PSO-LSTM and SSA-LSTM), are first developed and applied to determine the significance input combination to the changes of PM2.5 concentration at respective target stations. Then, the input configuration that gives the best forecasting accuracy was selected for subsequent experiments using enhanced approaches based on ensemble empirical mode decomposition (EEMD-PSO-LSTM and EEMD-SSA-LSTM). Subsequently, this study also analyzed the contributions of the neighboring PM2.5 dataset to the fluctuation of PM2.5 concentration at target stations. It is found that EEMD-SSA-LSTM model of M5 improves other models in Batu Muda and Cheras by 2.65% and 20.00% for RMSE and 9.31% and 25.30% for MAE, respectively. The results indicate that the proposed model yields the highest forecasting accuracy compared to the other models, and additional information on neighboring PM2.5 significantly improves the forecasting accuracy at both target stations. Besides that, comparing the performance of the two optimization approaches, SSA provides better performance compared to PSO in optimizing LSTM hyperparameters to forecast PM2.5 concentration. � 2023 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo139233
dc.identifier.doi10.1016/j.jclepro.2023.139233
dc.identifier.scopus2-s2.0-85174055041
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85174055041&doi=10.1016%2fj.jclepro.2023.139233&partnerID=40&md5=afddd99360d81d011d27042b5d8b1538
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/33903
dc.identifier.volume427
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleJournal of Cleaner Production
dc.subjectAir pollution
dc.subjectArtificial intelligence
dc.subjectDeep learning
dc.subjectOptimization
dc.subjectParticulate matter
dc.subjectAir quality
dc.subjectEmpirical mode decomposition
dc.subjectEnergy utilization
dc.subjectForecasting
dc.subjectHealth risks
dc.subjectLearning algorithms
dc.subjectLong short-term memory
dc.subjectParticles (particulate matter)
dc.subjectPopulation statistics
dc.subjectSustainable development
dc.subjectTraffic congestion
dc.subjectAir pollution forecasting
dc.subjectAir quality monitoring stations
dc.subjectDeep learning
dc.subjectFine particulate matter
dc.subjectForecasting accuracy
dc.subjectOptimisations
dc.subjectOptimization algorithms
dc.subjectParticulate Matter
dc.subjectPerformance
dc.subjectPM 2.5
dc.subjectParticle swarm optimization (PSO)
dc.titleForecasting of fine particulate matter based on LSTM and optimization algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
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