[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122223-en":3,"doc-seo-122223-105":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},122223,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Prediction of particulate matter PM2.5 level in the air of Islamabad, Pakistan by using machine learning and deep learning approaches - Journal Article","Air pollution presents a major global health threat, and particulate matter PM2.5 is highlighted as the most harmful component to the respiratory system. This study targets PM2.5 prediction for Islamabad, Pakistan, employing both machine learning and deep learning models. Decision Tree and Random Forest are compared with Multi-Layer Neural Network (MLNN), LSTM, RNN, and GRU, evaluating performance using R2, MAE, RMSE, and RRMSE, then ranking models by compromise programming.","Journal of Air Pollution and Health (Winter 2025); 10(1): 37-60  \nOriginal Article  \nAvailable online at [http://japh.tums.ac.ir](http://japh.tums.ac.ir)  \nPrediction of particulate matter PM2.5 level in the air of Islamabad, Pakistan by using machine learning and deep learning approaches  \nMuhammad Waqas1,*, Shahid Noor Jan1, Basir Ullah1, Afed Ullah Khan1, Ateeq Ur Rauf1, Bakht Niaz Khan2  \n1 Department of Civil Engineering, Bannu Campus, University of Engineering and Technology Peshawar, Bannu, Pakistan  \n2 Water and Sanitation Services Bannu (WSSB), Khyber Pakhtunkhwa, Pakistan  \nA R T I C L E I N F O R M A T I O N  \nArticle Chronology:  \nReceived 04 November 2024  \nRevised 04 January 2025  \nAccepted 25 February 2025  \nPublished 29 March 2025  \nKeywords:  \nLong and short-term memory (LSTM); Deep learning; Air quality; Machine learning; Multi-layers neural network (MLNN)  \nCORRESPONDING AUTHOR:  \n[engr.muhammadwaqas88774@gmail.com](engr.muhammadwaqas88774@gmail.com)[ ](engr.muhammadwaqas88774@gmail.com)[Tel :](Tel :) (+92 91) 9216796-8  \nFax : (+92 91) 9216663  \nABSTRACT  \nIntroduction: Air pollution is a significant global health challenge, contributing to the deaths of millions of people annually. Among these pollutants, Particulate Matter (PM2.5) is the most harmful to the respiratory system causing serious health problems. This study focused on predicting PM2.5 in the air of Islamabad, capital of Pakistan by using machine learning and deep learning models.  \nMaterials and methods: Two machine learning models (Decision Tree and Random Forest) and four deep learning models including Multi-Layer Neural Network (MLNN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU) are used in the study. Each model's performance was assessed by using statistical indicators including coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Relative Root Mean Square Error (RRMSE) . These models are also ranked based on their performance by compromise programming technique.  \nResults: Machine learning models performed better in the training phase by achieving higher R2 values of 0.98 and 0.97 but couldn’t maintain the same performance in the testing phase. Whereas the deep learning models performed best in both the training and testing phases. MLNN model attained higher R2 value of 0.98 in training and 0.88 in testing and is evaluated as top-ranked prediction model in predicting particulate matter PM2.5. Whereas, LSTM, GRU, RNN, Decision Tree, and Random Forest are placed at the 2nd, 3rd, 4th, 5th, and 6th positions having R2 values of 0 . 86, 0 . 87, 0 . 82, 0 .99, and 0.97 during training and 0.71, 0.69, 0.69, 0.75, and 0.85 respectively during testing.  \nConclusion: Deep learning models, especially MLNN, showed strong performance in predicting PM2.5 as compared to the machine learning models.  \nPlease cite this article as: Waqas M, Jan ShN, Ullah B, Khan AU, Rauf AU, Niaz Khan B. Prediction of particulate matter PM2.5 level in the air of Islamabad, Pakistan by using machine learning and deep learning approaches. Journal of Air Pollution and Health. 2025;10(1): 37-60.  \nCopyright © 2025 Tehran University of Medical Sciences. Published by Tehran University of Medical Sciences.  \nThis work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International license ([https://creativecommons.org/licenses/](https://creativecommons.org/licenses/)[ ](https://creativecommons.org/licenses/)[by-nc/4.0/](by-nc/4.0/)) . Noncommercial uses of the work are permitted, provided the original work is properly cited.  \nIntroduction  \nAir quality has become a pressing global concern due to its significant impact on human health, ecosystems, and economies [1] . The degradation of air quality, particularly in urbanized and industrialized regions, has reached alarming levels. Multiple factors, including rapid population growth, increased combustion of fossil fuels, vehi","cbCair6hWrlPbDVa","https://ap.wps.com/l/cbCair6hWrlPbDVa","pdf",5299440,1,24,"English","en",105,"# Abstract\n## Introduction\n## Materials and methods\n## Results\n## Conclusion\n# Keywords\n## Model evaluation metrics","[{\"question\":\"Which models are used to predict PM2.5 in Islamabad, Pakistan?\",\"answer\":\"The study employs two machine learning models (Decision Tree and Random Forest) and four deep learning models: MLNN, LSTM, RNN, and GRU.\"},{\"question\":\"How is model performance evaluated in the study?\",\"answer\":\"Performance is assessed using R2, MAE, RMSE, and RRMSE, with models ranked based on compromise programming technique.\"},{\"question\":\"Which approach performs best for PM2.5 prediction?\",\"answer\":\"Deep learning models perform best overall, with MLNN achieving the highest predictive performance across training and testing phases.\"}]","Prediction of particulate matter PM2.5 level in the air of Islamabad, Pakistan by using machine learning and deep learning approaches - 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