An Efficient Air Quality Detection System Using Sequential Forward Selection- Based ML Algorithms

An Efficient Air Quality Detection System Using Sequential Forward Selection- Based ML Algorithms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An Efficient Air Quality Detection System Using Sequential Forward Selection- Based ML Algorithms Nermeen Gamal Rezk, Amged Sayed, Ezz El-Din Hemdan, Heba El-Behery This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4697891/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Air pollution has thus exceeded the anticipated safety limit due to the growing usage of automobiles, the manufacturing sector, and the production of pollutants from other human activities. It is considered one of the major environmental risks to humankind on Earth. Nowadays, monitoring and forecasting air quality is significant, particularly in high-level pollution countries. In contrast to traditional methodologies, predictive technologies based on machine learning approaches be the most effective instruments for analyzing such contemporary threats. Therefore, this paper presents multiclass classification using two feature selection techniques namely Sequential Forward Selection (SFS) and Filter with different Machine Learning and Ensemble techniques to predict the air quality. Therefore, intensive research is conducted in novel techniques such as Wrapper and Filter feature selection methods to make sure that the most relevant features are on datasets for the solution of the air quality problem. The results of the considered framework reveal that the Wrapper feature selection technique provides superior performance compared to various Filter feature selection with different ML methods, including AdaBoost Classifier, Extra Tree Classifier, KNN, RF, GB, and Bagging Classifier for efficiently determining the Air Quality Index (AQI). Its important goal is to visualize the air quality datasets to understand and see the hidden sight in datasets. These models' performances are assessed and compared using predetermined performance metrics. The AdaBoost Classifier model with Filter selection has the lowest accuracy, while the Random Forest Classifier model with Wrapper feature selection achieves the highest accuracy with 78.4% and 99.99% respectively. Based on the raw data set, it was noted that the F1-score, Recall, and Precision values of the Random Forest model Wrapper Feature selection achieve 99.96%, 99.97%, and 99.98% respectively. Therefore, the experimental results undoubtedly show the supremacy of the proposed approach, providing a practical, reliable, and robust tool to effectively determine the Air Quality Index (AQI) Air Quality Index Machine Learning Ensemble Learning Feature Selection Feature Scaling Feature Encoding Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4697891","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":338048923,"identity":"2821f203-5dc7-4c85-b040-d11a8bd1e46d","order_by":0,"name":"Nermeen Gamal Rezk","email":"data:image/png;base64,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","orcid":"","institution":"Kafrelsheikh University","correspondingAuthor":true,"prefix":"","firstName":"Nermeen","middleName":"Gamal","lastName":"Rezk","suffix":""},{"id":338048926,"identity":"ab82c420-8706-4cec-a2a5-b8836cc82814","order_by":1,"name":"Amged Sayed","email":"","orcid":"","institution":"Menoufia University","correspondingAuthor":false,"prefix":"","firstName":"Amged","middleName":"","lastName":"Sayed","suffix":""},{"id":338048928,"identity":"14422cbd-1572-4794-a330-8a5127c44763","order_by":2,"name":"Ezz El-Din Hemdan","email":"","orcid":"","institution":"Menoufia University","correspondingAuthor":false,"prefix":"","firstName":"Ezz","middleName":"El-Din","lastName":"Hemdan","suffix":""},{"id":338048930,"identity":"1deeb019-5d4a-44bb-8080-7b1c2b389bcf","order_by":3,"name":"Heba El-Behery","email":"","orcid":"","institution":"Kafrelsheikh University","correspondingAuthor":false,"prefix":"","firstName":"Heba","middleName":"","lastName":"El-Behery","suffix":""}],"badges":[],"createdAt":"2024-07-06 18:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4697891/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4697891/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66355574,"identity":"68d2e0bf-1119-477b-84da-9670a2a2c80d","added_by":"auto","created_at":"2024-10-10 21:31:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1000708,"visible":true,"origin":"","legend":"","description":"","filename":"AIRPOLLUTION1772024.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4697891/v1_covered_02d7ec29-68a7-4fa8-aa74-a08e339cd97a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Efficient Air Quality Detection System Using Sequential Forward Selection- Based ML Algorithms","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Air Quality Index, Machine Learning, Ensemble Learning, Feature Selection, Feature Scaling, Feature Encoding","lastPublishedDoi":"10.21203/rs.3.rs-4697891/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4697891/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAir pollution has thus exceeded the anticipated safety limit due to the growing usage of automobiles, the manufacturing sector, and the production of pollutants from other human activities. It is considered one of the major environmental risks to humankind on Earth. Nowadays, monitoring and forecasting air quality is significant, particularly in high-level pollution countries. In contrast to traditional methodologies, predictive technologies based on machine learning approaches be the most effective instruments for analyzing such contemporary threats. Therefore, this paper presents multiclass classification using two feature selection techniques namely Sequential Forward Selection (SFS) and Filter with different Machine Learning and Ensemble techniques to predict the air quality. Therefore, intensive research is conducted in novel techniques such as Wrapper and Filter feature selection methods to make sure that the most relevant features are on datasets for the solution of the air quality problem. The results of the considered framework reveal that the Wrapper feature selection technique provides superior performance compared to various Filter feature selection with different ML methods, including AdaBoost Classifier, Extra Tree Classifier, KNN, RF, GB, and Bagging Classifier for efficiently determining the Air Quality Index (AQI). Its important goal is to visualize the air quality datasets to understand and see the hidden sight in datasets. These models' performances are assessed and compared using predetermined performance metrics. The AdaBoost Classifier model with Filter selection has the lowest accuracy, while the Random Forest Classifier model with Wrapper feature selection achieves the highest accuracy with 78.4% and 99.99% respectively. Based on the raw data set, it was noted that the F1-score, Recall, and Precision values of the Random Forest model Wrapper Feature selection achieve 99.96%, 99.97%, and 99.98% respectively. Therefore, the experimental results undoubtedly show the supremacy of the proposed approach, providing a practical, reliable, and robust tool to effectively determine the Air Quality Index (AQI)\u003c/p\u003e","manuscriptTitle":"An Efficient Air Quality Detection System Using Sequential Forward Selection- Based ML Algorithms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-12 19:36:05","doi":"10.21203/rs.3.rs-4697891/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8c49a7bf-f79f-43a4-80ea-3d8bf6ccf00a","owner":[],"postedDate":"August 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-10T21:23:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-12 19:36:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4697891","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4697891","identity":"rs-4697891","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}