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Artificial Intelligence-assisted Air Quality Monitoring For Smart City Management

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dc.contributor.authorEn Xin Neoen_US
dc.contributor.authorKhairunnisa Hasikinen_US
dc.contributor.authorKhin Wee Laien_US
dc.contributor.authorMohd Istajib Mokhtaren_US
dc.contributor.authorMuhammad Mokhzaini Azizanen_US
dc.contributor.authorHanee Farzana Hizaddinen_US
dc.contributor.authorSarah Abdul Razaken_US
dc.contributor.authorYantoen_US
dc.date.accessioned2024-05-29T02:29:16Z
dc.date.available2024-05-29T02:29:16Z
dc.date.issued2023
dc.date.submitted2024-2-20
dc.description.abstractBackground. The environment has been significantly impacted by rapid urbaniza- tion, leading to a need for changes in climate change and pollution indicators. The 4IR offers a potential solution to efficiently manage these impacts. Smart city ecosys- tems can provide well-designed, sustainable, and safe cities that enable holistic climate change and global warming solutions through various community-centred initiatives. These include smart planning techniques, smart environment monitoring, and smart governance. An air quality intelligence platform, which operates as a complete mea- surement site for monitoring and governing air quality, has shown promising results in providing actionable insights. This article aims to highlight the potential of ma- chine learning models in predicting air quality, providing data-driven strategic and sustainable solutions for smart cities. Methods. This study proposed an end-to-end air quality predictive model for smart city applications, utilizing four machine learning techniques and two deep learning techniques. These include Ada Boost, SVR, RF, KNN, MLP regressor and LSTM. The study was conducted in four different urban cities in Selangor, Malaysia, including Petaling Jaya, Banting, Klang, and Shah Alam. The model considered the air qual- ity data of various pollution markers such as PM2.5, PM10, O3, and CO. Additionally, meteorological data including wind speed and wind direction were also considered, and their interactions with the pollutant markers were quantified. The study aimed to determine the correlation variance of the dependent variable in predicting air pol- lution and proposed a feature optimization process to reduce dimensionality and re- move irrelevant features to enhance the prediction of PM2.5, improving the existing LSTM model. The study estimates the concentration of pollutants in the air based on training and highlights the contribution of feature optimization in air quality predic- tions through feature dimension reductions. Results. In this section, the results of predicting the concentration of pollutants (PM2.5, PM10, O3, and CO) in the air are presented in R2 and RMSE. In predicting the PM10 and PM2.5 concentration, LSTM performed the best overall high R2 values in the four study areas with the R2 values of 0.998, 0.995, 0.918, and 0.993 in Banting, How to cite this article Neo EX, Hasikin K, Lai KW, Mokhtar MI, Azizan MM, Hizaddin HF, Razak SA, Yanto . 2023. Artificial intelligence-assisted air quality monitoring for smart city management. PeerJ Comput. Sci. 9:e1306 http://doi.org/10.7717/peerj-cs.1306 Petaling, Klang and Shah Alam stations, respectively. The study indicated that among the studied pollution markers, PM2.5, PM10, NO2, wind speed and humidity are the most important elements to monitor. By reducing the number of features used in the model the proposed feature optimization process can make the model more interpretable and provide insights into the most critical factor affecting air quality. Findings from this study can aid policymakers in understanding the underlying causes of air pollution and develop more effective smart strategies for reducing pollution levels.en_US
dc.identifier.citationNeo EX, Hasikin K, Lai KW, Mokhtar MI, Azizan MM, Hizaddin HF, Razak SA, Yanto. 2023. Artificial intelligence-assisted air quality monitoring for smart city management. PeerJ Computer Science 9:e1306 https://doi.org/10.7717/peerj-cs.1306en_US
dc.identifier.doi10.7717/peerj-cs.1306
dc.identifier.epage35
dc.identifier.issn2376-5992
dc.identifier.issuee1306
dc.identifier.spage1
dc.identifier.urihttps://peerj.com/articles/cs-1306/#
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85160785869&origin=resultslist&sort=plf-f&src=s&sid=8cf049cb0534d9dab2737fdd866039ca&sot=b&sdt=b&s=TITLE-ABS-KEY%28Artificial+intelligence-assisted+air+quality+monitoring+for+smart+city+management%29&sl=100&sessionSearchId=8cf049cb0534d9dab2737fdd866039ca&relpos=0
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/10806
dc.identifier.volume9
dc.language.isoen_USen_US
dc.publisherPeerJ Publishingen_US
dc.relation.ispartofPeerJ Computer Scienceen_US
dc.subjectArtificial Intelligence, Data Mining and Machine Learning, Data Science, Neural Networks, Internet of Thingsen_US
dc.subjectAI, Air quality monitoring, Smart cities, Sustainability, Air quality managementen_US
dc.titleArtificial Intelligence-assisted Air Quality Monitoring For Smart City Managementen_US
dc.typeArticleen_US
dspace.entity.typePublication

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