Modeling watershed nutrient concentrations with AutoML-Reference-Cited by-同舟云学术

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Modeling watershed nutrient concentrations with AutoML

Published:2020-09-22 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 10th International Conference on Climate Informatics
language:
Short-container-title:

Author:

Kim Grace E.¹,Steller Moritz¹,Olson Sarah¹

Affiliation:

1. Booz Allen Hamilton, US

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3429309.3429322

Reference14 articles.

1. Chesapeake Bay Program. [n.d.]. CBP Water Quality Database. http://data.chesapeakebay.net/WaterQuality. Chesapeake Bay Program. [n.d.]. CBP Water Quality Database. http://data.chesapeakebay.net/WaterQuality.

2. Chesapeake Monitoring Cooperative. [n.d.]. Welcome to the Chesapeake Data Explorer!: Water Quality. https://cmc.vims.edu/. Chesapeake Monitoring Cooperative. [n.d.]. Welcome to the Chesapeake Data Explorer!: Water Quality. https://cmc.vims.edu/.

3. Chesapeake Executive Council. 2014. Chesapeake Bay watershed agreement. (2014). Chesapeake Executive Council. 2014. Chesapeake Bay watershed agreement. (2014).

4. Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition

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1. Using automated machine learning for the upscaling of gross primary productivity;Biogeosciences;2024-05-24

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