Forecasting health outcomes of Air Pollution: A Statistical review of modelling techniques and applications
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| Autores principales: | , |
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
| Publicado: |
Zenodo
2025
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| _version_ | 1866902290156224512 |
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| author | Sonone, Pranjali Subhash Khamborkar, Abhay Kamlakar |
| author_facet | Sonone, Pranjali Subhash Khamborkar, Abhay Kamlakar |
| contents | <p>This review summarises all recent papers related to the prediction of diseases caused by air pollution. Most of the papers identified through the literature review focused either on health risk prediction alone or air pollutant prediction in various regions. However, very few research articles are based on predictive models that provide health risk predictions and also assess the effects on health. Therefore, the primary objective of this review is to identify models that incorporate both air pollution data and health data to predict diseases. This review encompasses the shift from traditional models to machine learning models in forecasting. This study will be beneficial for future research aimed at identifying diseases caused solely by specific air pollutants.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17529228 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Forecasting health outcomes of Air Pollution: A Statistical review of modelling techniques and applications Sonone, Pranjali Subhash Khamborkar, Abhay Kamlakar Review of Air Pollution Studies Machine Learning Air Pollution Health Risk Prediction of Disease <p>This review summarises all recent papers related to the prediction of diseases caused by air pollution. Most of the papers identified through the literature review focused either on health risk prediction alone or air pollutant prediction in various regions. However, very few research articles are based on predictive models that provide health risk predictions and also assess the effects on health. Therefore, the primary objective of this review is to identify models that incorporate both air pollution data and health data to predict diseases. This review encompasses the shift from traditional models to machine learning models in forecasting. This study will be beneficial for future research aimed at identifying diseases caused solely by specific air pollutants.</p> |
| title | Forecasting health outcomes of Air Pollution: A Statistical review of modelling techniques and applications |
| topic | Review of Air Pollution Studies Machine Learning Air Pollution Health Risk Prediction of Disease |
| url | https://doi.org/10.5281/zenodo.17529228 |