A pruned feed-forward neural network (pruned-FNN) approach to measure air pollution exposure-Reference-Cited by-同舟云学术

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A pruned feed-forward neural network (pruned-FNN) approach to measure air pollution exposure

Published:2023-09-11 Issue:10 Volume:195 Page:
ISSN:0167-6369
Container-title:Environmental Monitoring and Assessment
language:en
Short-container-title:Environ Monit Assess

Author:

Gong Xi^ORCID,Liu Lin,Huang Yanhong,Zou Bin^ORCID,Sun Yeran^ORCID,Luo Li^ORCID,Lin Yan^ORCID

Funder

National Institute of General Medical Sciences

University of New Mexico Office of the Vice President for Research

University of New Mexico, A&S Interdisciplinary Science Cooperative through the Office of Research

National Cancer Institutes

National Institute of Environmental Health Sciences

National Institute on Minority Health and Health Disparities

National Science Foundation

Publisher

Springer Science and Business Media LLC

Subject

Management, Monitoring, Policy and Law,Pollution,General Environmental Science,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s10661-023-11814-5.pdf

Reference65 articles.

1. Adams, M. D., & Kanaroglou, P. S. (2016). Mapping real-time air pollution health risk for environmental management: Combining mobile and stationary air pollution monitoring with neural network models. Journal of Environmental Management, 168, 133–141. https://doi.org/10.1016/j.jenvman.2015.12.012

2. Banerjee, K., Bali, V., Nawaz, N., Bali, S., Mathur, S., Mishra, R. K., & Rani, S. (2022). A machine-learning approach for prediction of water contamination using latitude, longitude, and elevation. Water (Switzerland), 14(5). https://doi.org/10.3390/w14050728

3. Bell, M. L. (2006). The use of ambient air quality modeling to estimate individual and population exposure for human health research: A case study of ozone in the Northern Georgia Region of the United States. Environment International, 32(5), 586–593. https://doi.org/10.1016/j.envint.2006.01.005

4. Bishop, C. M. (2006). Pattern recognition and machine learning. In EAI/Springer Innovations in Communication and Computing. https://doi.org/10.1007/978-3-030-57077-4_11

5. Blanes-Vidal, V., Cantuaria, M. L., & Nadimi, E. S. (2017). A novel approach for exposure assessment in air pollution epidemiological studies using neuro-fuzzy inference systems: Comparison of exposure estimates and exposure-health associations. Environmental Research, 154(January), 196–203. https://doi.org/10.1016/j.envres.2016.12.028

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