Predicting Air Quality Index in Accra using Machine Learning

Predicting Air Quality Index in Accra using Machine Learning | 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 Predicting Air Quality Index in Accra using Machine Learning Stephen Edward Moore, Aaron P. Antwi, Eric Onyame, Sianou E. Houénafa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5774869/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 quality is a significant public health issue, and accurate predictions of the Air Quality Index (AQI) are crucial for timely interventions. This study explores the use of supervised machine learning algorithms to forecast AQI across different neighborhoods in Accra, Ghana. Six models including Random Forest , CatBoost, Support Vector Regression (SVR), Linear Regression, Ridge and Lasso Regressions, were evaluated. Data from the Breathe Accra platform, encompassing pollutants and weather conditions in five neighborhoods, were preprocessed through data cleaning, feature selection, and normalization. Model performance was assessed using the coefficient of determination (R 2 ), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). While the initial performance of the models was less satisfactory, the incorporation of wavelet transform preprocessing significantly enhanced the results. This improvement was particularly notable for the area of Korle Bu, where the CatBoost model's R 2 increased from 0.32 to 0.58, RMSE decreased from 29.03 to 22.79, and MAE dropped from 22.11 to 17.01. On average, all models except SVR performed well across all areas, as evidenced by the evaluation metrics. These findings have direct implications for enhancing air quality management and policymaking in the city of Accra, where accurate AQI predictions are vital for effective public health interventions and environmental planning. Artificial Intelligence and Machine Learning Environmental Chemistry Environmental Policy Air quality Accra machine learning climate change wavelet transform Full Text Additional Declarations The authors declare no competing interests. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5774869","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":398376295,"identity":"2dc51221-fd72-4f79-8e6a-3862b8e5ba85","order_by":0,"name":"Stephen Edward Moore","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYJACZgSzAkJJAHECTvVsKFrOkKyFsY0ILfLzm489Lqi4w6Db3nzsw8d5h+X5G5gP3uZhqM3DpcXgGFu68YwzzxjMzhxLnjlz22HDGQfYkq15GI4X49TCxmMmzdt2mMHsRo4xM++2wwkGDEARHoZjiQ24HNbG/02a9x9Uy985IC1AEXxaGI7xsEnzNkC1MDaAbWEDaqnBqcXgWJq5Mc+xwzwgvzD2HEs3nHGYzdhyjsEB3A5rPvzsMU/NYTmz482HGX7UWMvztzc/vPGmog63w8Axw8DAg+CDo8ngMG4NUC0YoA6fllEwCkbBKBhZAAChwVJBYTo2fQAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Mathematics, University of Cape Coast","correspondingAuthor":true,"prefix":"","firstName":"Stephen","middleName":"Edward","lastName":"Moore","suffix":""},{"id":398376296,"identity":"ba542e3f-a727-4c5d-b08e-f925eaca57f2","order_by":1,"name":"Aaron P. 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This study explores the use of supervised machine learning algorithms to forecast AQI across different neighborhoods in Accra, Ghana. Six models \u0026nbsp;including Random Forest , CatBoost, Support Vector Regression (SVR), Linear Regression, Ridge and Lasso Regressions, were evaluated. Data from the Breathe Accra platform, encompassing pollutants and weather conditions in five neighborhoods, were preprocessed through data cleaning, feature selection, and normalization. Model performance was assessed using the coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). While the initial performance of the models was less satisfactory, the incorporation of wavelet transform preprocessing significantly enhanced the results. 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