Using Random Forest, a machine learning approach to predict nitrogen, phosphorus, and sediment event mean concentrations in urban runoff-Reference-Cited by-同舟云学术

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Using Random Forest, a machine learning approach to predict nitrogen, phosphorus, and sediment event mean concentrations in urban runoff

Published:2022-09 Issue: Volume:317 Page:115412
ISSN:0301-4797
Container-title:Journal of Environmental Management
language:en
Short-container-title:Journal of Environmental Management

Author:

Behrouz Mina Shahed^ORCID,Yazdi Mohammad Nayeb,Sample David J.^ORCID

Funder

Virginia Water Resources Research Center

US Department of Agriculture

Virginia Agricultural Experiment Station, Virginia Polytechnic Institute and State University

U.S. Geological Survey

Government of South Australia

National Institute of Food and Agriculture

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Waste Management and Disposal,General Medicine,Environmental Engineering

Reference59 articles.

1. Sources of nutrients impacting surface waters in Florida: a review;Badruzzaman;J. Environ. Manag.,2012

2. Comparing stormwater quality and watershed typologies across the United States: a machine learning approach;Balderas Guzman;Water Res.,2022

3. What are the relevant sources and factors affecting event mean concentrations (EMCs) of nutrients and sediment in stormwater?;Behrouz;Sci. Total Environ.,2022

4. A random forest guided tour;Biau;Test,2016

5. Random forests;Breiman;Mach. Learn.,2001

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