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Predictive modelling of nitrogen dioxide using soft computing techniques in the Agra, Uttar Pradesh, India

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dc.citedby2
dc.contributor.authorSihag P.en_US
dc.contributor.authorMehta T.en_US
dc.contributor.authorSammen S.S.en_US
dc.contributor.authorPande C.B.en_US
dc.contributor.authorPuri D.en_US
dc.contributor.authorRadwan N.en_US
dc.contributor.authorid57195985799en_US
dc.contributor.authorid58135855600en_US
dc.contributor.authorid57192093108en_US
dc.contributor.authorid57193547008en_US
dc.contributor.authorid57190749797en_US
dc.contributor.authorid56763877500en_US
dc.date.accessioned2025-03-03T07:43:49Z
dc.date.available2025-03-03T07:43:49Z
dc.date.issued2024
dc.description.abstractNitrogen dioxide (NO2) is one of the air pollutants which aggravates the human health as well as causes environmental issues. It is more causes respiratory problems due to acid rains. Agra is a major tourist destination spot in India also similarly air pollution also increased growing urbanization and traffic reflux. The current study aims to predicted the NO2 episodes in the Agra city using soft computing models namely, M5P, Random Forest (RF), Group method of data handling (GMDH), Multivariate adaptive regression (MARS), Reduced error pruning tree (REP Tree) and Random tree (RT). The models were generated using 1116 observations, from 2015 to 2020 with input parameters such as Particulate matter (PM2.5), Nitrogen monoxide (NO), Oxides of nitrogen (NOX), Sulphur dioxide (SO2), Carbon monoxide (CO), Ozone (O), Benzene (Be), Toluene, Relative humidity (RH), Wind speed (WS), Wind direction (WD), Solar radiation (SR), Barometric pressure (BP) and Xylene. The performance of each model was evaluated based on the six statistical indices, namely correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), normalized root of mean squared relative error (NRMSE), Willmott's Index (WI), and Legates and McCabe's Index (LMI). The performance evaluation models results of study area, Box plot and Taylor's diagram indicated that M5P is the outperforming model among others with testing CC = 0.9543, RMSE = 5.8006, MAE = 3,9204, NRMSE = 0.1512, WI = 0.9744, and LMI = 0.7549. Based on the sensitivity analysis indicated that NOx is the most influential parameter followed by WD and CO. These results of study area can be helpful to understanding the air pollution causes, health issues, and future NO2 levels around the study area with useful results for air pollution monitoring policy and development. ? 2024 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo103589
dc.identifier.doi10.1016/j.pce.2024.103589
dc.identifier.scopus2-s2.0-85189745092
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85189745092&doi=10.1016%2fj.pce.2024.103589&partnerID=40&md5=f99d043dff4a9af5229ca40f5f2c4394
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36673
dc.identifier.volume134
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitlePhysics and Chemistry of the Earth
dc.subjectAgra
dc.subjectIndia
dc.subjectUttar Pradesh
dc.subjectAcid rain
dc.subjectAir pollution
dc.subjectAtmospheric pressure
dc.subjectCarbon monoxide
dc.subjectData handling
dc.subjectErrors
dc.subjectForestry
dc.subjectLinear matrix inequalities
dc.subjectMean square error
dc.subjectNitrogen oxides
dc.subjectRecurrent neural networks
dc.subjectSensitivity analysis
dc.subjectSoft computing
dc.subjectWind
dc.subjectGroup method of data handling
dc.subjectM5P
dc.subjectMultivariate adaptive regression
dc.subjectNitrogen monoxide
dc.subjectNitrogen monoxide2
dc.subjectRandom forests
dc.subjectRandom tree
dc.subjectReduced error pruning tree
dc.subjectReduced-error pruning
dc.subjectStudy areas
dc.subjectatmospheric chemistry
dc.subjectatmospheric pollution
dc.subjectcomputer simulation
dc.subjectmultivariate analysis
dc.subjectnitrogen dioxide
dc.subjectregression analysis
dc.subjectrelative humidity
dc.subjectSulfur dioxide
dc.titlePredictive modelling of nitrogen dioxide using soft computing techniques in the Agra, Uttar Pradesh, Indiaen_US
dc.typeArticleen_US
dspace.entity.typePublication
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