Deep learning-based ensemble modeling of Vibrio parahaemolyticus concentration in marine environment-Reference-Cited by-同舟云学术

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Deep learning-based ensemble modeling of Vibrio parahaemolyticus concentration in marine environment

Published:2022-12-24 Issue:1 Volume:195 Page:
ISSN:0167-6369
Container-title:Environmental Monitoring and Assessment
language:en
Short-container-title:Environ Monit Assess

Author:

Namadi Peyman,Deng Zhiqiang

Funder

National Aeronautics and Space Administration

Louisiana Board of Regents

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/s10661-022-10836-9.pdf

Reference28 articles.

1. Chenar, S. S., & Deng, Z. (2018a). Development of artificial intelligence approach to forecasting oyster norovirus outbreaks along Gulf of Mexico coast. Environment International, 111, 212–223.

2. Chenar, S. S., & Deng, Z. (2018b). Development of genetic programming-based model for predicting oyster norovirus outbreak risks. Water Research, 128, 20–37.

3. Chenar, S. S., & Deng, Z. (2021). Hybrid modeling and prediction of oyster norovirus outbreaks. Journal of Water and Health, 19(2), 254–266. https://doi.org/10.2166/wh.2021.251

4. Daniels, N. A., MacKinnon, L., Bishop, R., Altekruse, S., Ray, B., Hammond, R. M., Thompson, S., Wilson, S., Bean, N. H., & Griffin, P. M. (2000). Vibrio parahaemolyticus infections in the United States, 1973–1998. Journal of Infectious Diseases, 181, 1661–1666.

5. Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874.

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