Modeling freshwater plankton community dynamics with static and dynamic interactions using graph convolution embedded long short-term memory-Reference-Cited by-同舟云学术

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Modeling freshwater plankton community dynamics with static and dynamic interactions using graph convolution embedded long short-term memory

Published:2024-11 Issue: Volume:266 Page:122401
ISSN:0043-1354
Container-title:Water Research
language:en
Short-container-title:Water Research

Author:

Kim Hyo Gyeom^ORCID,Jung Eun-Young,Jeong Heewon,Son Heejong^ORCID,Baek Sang-Soo,Cho Kyung Hwa

Funder

Korea Environmental Industry and Technology Institute

Korea Ministry of Education

National Research Foundation of Korea

Ministry of Education - Singapore

Ministry of Environment

Publisher

Elsevier BV

Reference71 articles.

1. Deep learning-based algorithms for long-term prediction of chlorophyll-a in catchment streams;Abbas;J. Hydrol. (Amst),2023

2. Operational early warning systems for water-related hazards in Europe;Alfieri;Environ. Sci. Policy.,2012

3. Hydrological drought forecasting using a deep transformer model;Amanambu;Water. (Basel),2022

4. Hierarchical deep learning model to simulate phytoplankton at phylum/class and genus levels and zooplankton at the genus level;Baek;Water. Res.,2022

5. Environmental multi-scale effects on zooplankton inter-specific synchrony;Buttay;Limnol. Oceanogr.,2017

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