Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm
Abstract Fine particulate matter (PM2.5) is a significant air pollutant that drives the most chronic health problems and premature mortality in big metropolitans such as Delhi. In such a context, accurate prediction of PM2.5 concentration is critical for raising public awareness, allowing sensitive...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-47492-z |
| _version_ | 1797415313608802304 |
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| author | Adil Masood Mohammed Majeed Hameed Aman Srivastava Quoc Bao Pham Kafeel Ahmad Siti Fatin Mohd Razali Souad Ahmad Baowidan |
| author_facet | Adil Masood Mohammed Majeed Hameed Aman Srivastava Quoc Bao Pham Kafeel Ahmad Siti Fatin Mohd Razali Souad Ahmad Baowidan |
| author_sort | Adil Masood |
| collection | DOAJ |
| description | Abstract Fine particulate matter (PM2.5) is a significant air pollutant that drives the most chronic health problems and premature mortality in big metropolitans such as Delhi. In such a context, accurate prediction of PM2.5 concentration is critical for raising public awareness, allowing sensitive populations to plan ahead, and providing governments with information for public health alerts. This study applies a novel hybridization of extreme learning machine (ELM) with a snake optimization algorithm called the ELM-SO model to forecast PM2.5 concentrations. The model has been developed on air quality inputs and meteorological parameters. Furthermore, the ELM-SO hybrid model is compared with individual machine learning models, such as Support Vector Regression (SVR), Random Forest (RF), Extreme Learning Machines (ELM), Gradient Boosting Regressor (GBR), XGBoost, and a deep learning model known as Long Short-Term Memory networks (LSTM), in forecasting PM2.5 concentrations. The study results suggested that ELM-SO exhibited the highest level of predictive performance among the five models, with a testing value of squared correlation coefficient (R 2) of 0.928, and root mean square error of 30.325 µg/m3. The study's findings suggest that the ELM-SO technique is a valuable tool for accurately forecasting PM2.5 concentrations and could help advance the field of air quality forecasting. By developing state-of-the-art air pollution prediction models that incorporate ELM-SO, it may be possible to understand better and anticipate the effects of air pollution on human health and the environment. |
| first_indexed | 2024-03-09T05:46:56Z |
| format | Article |
| id | doaj.art-36c9a9a2068a40efaac59a0538040fa9 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T05:46:56Z |
| publishDate | 2023-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-36c9a9a2068a40efaac59a0538040fa92023-12-03T12:20:29ZengNature PortfolioScientific Reports2045-23222023-11-0113111710.1038/s41598-023-47492-zImproving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithmAdil Masood0Mohammed Majeed Hameed1Aman Srivastava2Quoc Bao Pham3Kafeel Ahmad4Siti Fatin Mohd Razali5Souad Ahmad Baowidan6Department of Civil Engineering, Jamia Millia Islamia UniversityDepartment of Civil Engineering, Al-Maarif University CollegeDepartment of Civil Engineering, Indian Institute of Technology (IIT) KharagpurFaculty of Natural Sciences, Institute of Earth Sciences, University of Silesia in KatowiceDepartment of Civil Engineering, Jamia Millia Islamia UniversityDepartment of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan MalaysiaInformation Technology Department Faculty of Computing and IT, King Abdulaziz UniversityAbstract Fine particulate matter (PM2.5) is a significant air pollutant that drives the most chronic health problems and premature mortality in big metropolitans such as Delhi. In such a context, accurate prediction of PM2.5 concentration is critical for raising public awareness, allowing sensitive populations to plan ahead, and providing governments with information for public health alerts. This study applies a novel hybridization of extreme learning machine (ELM) with a snake optimization algorithm called the ELM-SO model to forecast PM2.5 concentrations. The model has been developed on air quality inputs and meteorological parameters. Furthermore, the ELM-SO hybrid model is compared with individual machine learning models, such as Support Vector Regression (SVR), Random Forest (RF), Extreme Learning Machines (ELM), Gradient Boosting Regressor (GBR), XGBoost, and a deep learning model known as Long Short-Term Memory networks (LSTM), in forecasting PM2.5 concentrations. The study results suggested that ELM-SO exhibited the highest level of predictive performance among the five models, with a testing value of squared correlation coefficient (R 2) of 0.928, and root mean square error of 30.325 µg/m3. The study's findings suggest that the ELM-SO technique is a valuable tool for accurately forecasting PM2.5 concentrations and could help advance the field of air quality forecasting. By developing state-of-the-art air pollution prediction models that incorporate ELM-SO, it may be possible to understand better and anticipate the effects of air pollution on human health and the environment.https://doi.org/10.1038/s41598-023-47492-z |
| spellingShingle | Adil Masood Mohammed Majeed Hameed Aman Srivastava Quoc Bao Pham Kafeel Ahmad Siti Fatin Mohd Razali Souad Ahmad Baowidan Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm Scientific Reports |
| title | Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm |
| title_full | Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm |
| title_fullStr | Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm |
| title_full_unstemmed | Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm |
| title_short | Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm |
| title_sort | improving pm2 5 prediction in new delhi using a hybrid extreme learning machine coupled with snake optimization algorithm |
| url | https://doi.org/10.1038/s41598-023-47492-z |
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