Long short-term memory models of water quality in inland water environments

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Podrobná bibliografie
Vydáno v:	Water Research X Ročník 21; s. 100207
Hlavní autoři:	Juncheol Pyo, Yakov Pachepsky, Soobin Kim, Ather Abbas, Minjeong Kim, Young-Mi Kwon, Mayzonee Ligaray, Kyung Hwa Cho
Médium:	Journal Article
Jazyk:	japonština
Vydáno:	Elsevier BV 01.12.2023
Témata:	Artificial intelligence Artificial neural network Biochemistry Chemistry Computer science Convolutional neural network Deep Learning Deep learning models Electrical and Electronic Engineering Electricity Price and Load Forecasting Methods Engineering Ensemble LSTM Environmental Engineering Environmental Science Environmental technology. Sanitary engineering FOS: Electrical engineering, electronic engineering, information engineering FOS: Environmental engineering Gene Groundwater Level Forecasting Hydrological Modeling using Machine Learning Methods Inland water Load Forecasting Long short-term memory Machine learning Memory model Operating system Physical Sciences Physics Quantum mechanics Real-time Water Quality Monitoring and Aquaculture Management Review Article Robustness (evolution) Shared memory Short-Term Forecasting TD1-1066 Term (time) Time series Water quality Water Quality Monitoring Water Science and Technology
ISSN:	2589-9147, 2589-9147
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Popis
ISSN:	2589-9147 2589-9147
DOI:	10.1016/j.wroa.2023.100207