Long-term prediction of algal chlorophyll based on empirical models and the machine learning approach in relation to trophic variation in Juam Reservoir, Korea-Reference-Cited by-同舟云学术

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Long-term prediction of algal chlorophyll based on empirical models and the machine learning approach in relation to trophic variation in Juam Reservoir, Korea

Published:2024-06 Issue:11 Volume:10 Page:e31643
ISSN:2405-8440
Container-title:Heliyon
language:en
Short-container-title:Heliyon

Author:

Jin Sang-Hyeon,Jargal Namsrai,Khaing Thet Thet,Cho Min Jae,Choi Hyeji^ORCID,Ariunbold Bilguun,Donat Mnyagatwa Geofrey,Yoo Haechan,Mamun Md,An Kwang-Guk

Funder

Daejeon Green Environment Center

Publisher

Elsevier BV

Reference69 articles.

1. Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems;Lehner;Hydrol. Process.,2013

2. Factors influencing phosphorus in midcontinent impoundments (USA) and challenges for detecting abatement;Jones;Inland Waters,2022

3. Eutrophication: causes, consequences, and controls in aquatic ecosystems;Chislock;Nature Education Knowledge,2013

4. Reducing phosphorus to curb lake eutrophication is a success;Schindler;Environ. Sci. Technol.,2016

5. Freshwater Ecology: Concepts and Environmental Applications of Limnology;Dodds,2019

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Diverse interactions between bacteria and microalgae: A review for enhancing harmful algal bloom mitigation and biomass processing efficiency;Heliyon;2024-09

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