[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120887-en":3,"doc-seo-120887-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120887,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Optimized machine learning model for air quality index prediction in major cities in India - read online","Optimized machine learning is applied to forecast the Air Quality Index (AQI) of major Indian cities using air pollution datasets. The study combines Grey Wolf Optimization (GWO) with a Decision Tree (DT) regression approach to improve prediction accuracy. Experiments use cities including Delhi, Hyderabad, Kolkata, Bangalore, Visakhapatnam, and Chennai and evaluate performance with R-Square, RMSE, MSE, MAE, and accuracy. Results show the proposed model outperforming traditional methods such as k-nearest neighbors, Random Forest regressor, and Support vector regressor.","Citation:  \nNatarajan, SK and Shanmurthy, P and Arockiam, D and Balusamy, B and Selvarajan, S (2024) Optimized machine learning model for air quality index prediction in major cities in India. Scientific Reports, 14. pp. 1-18. ISSN 2045-2322 DOI: [https://doi.org/10.1038/s41598-024-54807-1](https://doi.org/10.1038/s41598-024-54807-1)  \nLink to Leeds Beckett Repository record:  \n[https://eprints.leedsbeckett.ac.uk/id/eprint/10666/](https://eprints.leedsbeckett.ac.uk/id/eprint/10666/)  \nDocument Version:  \nArticle (Published Version)  \nCreative Commons: Attribution 4.0  \n The Author(s) 2024  \nThe aim of the Leeds Beckett Repository is to provide open access to our research, as required by funder policies and permitted by publishers and copyright law.  \nThe Leeds Beckett repository holds a wide range of publications, each of which has been checked for copyright and the relevant embargo period has been applied by the Research Services team.  \nWe operate on a standard take-down policy. If you are the author or publisher of an output and you would like it removed from the repository, please contact us and we will investigate on a case-by-case basis.  \nEach thesis in the repository has been cleared where necessary by the author for third party copyright. If you would like a thesis to be removed from the repository or believe there is an issue with copyright, please contact us on [openaccess@leedsbeckett.ac.uk](openaccess@leedsbeckett.ac.uk) and we will investigate on a case-by-case basis.  \n[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nOptimized machine learning model for air quality index prediction in major cities in India  \nSuresh Kumar Natarajan1, Prakash Shanmurthy2, DanielArockiam3, Balamurugan Balusamy4 & Shitharth Selvarajan5*  \nIndustrial advancements and utilization of large amount of fossil fuels, vehicle pollution, and other calamities increases the Air Quality Index (AQI) of major cities in a drastic manner. Major cities AQI analysis is essential so that the government can take proper preventive, proactive measures to reduce air pollution. This research incorporates artificial intelligence inAQI prediction based on air pollution data. An optimized machine learning model which combines Grey Wolf Optimization (GWO) with the Decision Tree (DT) algorithm for accurate prediction of AQI in major cities of India. Air quality data available in the Kaggle repository is used for experimentation, and major cities like Delhi, Hyderabad, Kolkata, Bangalore, Visakhapatnam, and Chennai are considered for analysis. The proposed model performance is experimentally verified through metrics like R-Square, RMSE, MSE, MAE, and accuracy. Existing machine learning models, like k-nearest Neighbor, Random Forest regressor, and Support vector regressor, are compared with the proposed model. The proposed model attains better prediction performance compared to traditional machine learning algorithms with maximum accuracy of 88.98% for New Delhi city, 91.49% for Bangalore city, 94.48% for Kolkata, 97.66% for Hyderabad, 95.22% for Chennai and 97.68% for Visakhapatnam city.  \nKeywords Air pollution, Air quality index, Machine learning, Optimization algorithm, Grey-wolf optimization, Decision tree regression  \nAir pollution is one of the serious issues all around the globe. World Health Organization (WHO) reports that around 7 million people affected into numerous diseases because of air pollution. Air pollution increases the chances of asthma, heart issues, skin infections, eye diseases, throat infections, lung cancer, bronchitis diseases, etc., Long-term exposure of air pollutions may increase the chances of premature mortalities. Children might face development issues which includes impaired lung function and cognitive developments. Pregnant women might face issues in their pregnancy journey which includes low birth weight, premature births, etc., In addition to that diseases air pollution introduces serious threat t","cbCaiknyydfJVcrU","https://ap.wps.com/l/cbCaiknyydfJVcrU","pdf",4553471,1,19,"English","en",105,"# Air quality index and motivation\n# Proposed optimized model (GWO + decision tree)\n## Data source and target cities\n# Experimental evaluation\n## Metrics and comparative models\n# Results and city-wise performance","[{\"question\":\"What problem does the research address?\",\"answer\":\"The research targets accurate prediction of the Air Quality Index (AQI) for major cities in India to support timely preventive and proactive measures against air pollution.\"},{\"question\":\"How does the proposed model generate AQI predictions?\",\"answer\":\"It uses an optimized machine learning pipeline that combines Grey Wolf Optimization (GWO) with a Decision Tree (DT) algorithm for AQI prediction.\"},{\"question\":\"Which evaluation metrics and comparison models are used?\",\"answer\":\"Performance is assessed with R-Square, RMSE, MSE, MAE, and accuracy, and the approach is compared with k-nearest neighbor, Random Forest regressor, and Support vector regressor models.\"}]","Optimized machine learning model for air quality index prediction in major cities in India - 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