Long-term stagnation monitoring using machine learning: comparison of artificial neural network model and convolution neural network model-Reference-Cited by-同舟云学术

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Long-term stagnation monitoring using machine learning: comparison of artificial neural network model and convolution neural network model

Published:2022-04-20 Issue:7 Volume:36 Page:2117-2130
ISSN:0920-4741
Container-title:Water Resources Management
language:en
Short-container-title:Water Resour Manage

Author:

Lee Jaiyeop,Kim Ilho^ORCID

Funder

Korea Institute of Civil Engineering and Building Technology, KICT

Publisher

Springer Science and Business Media LLC

Subject

Water Science and Technology,Civil and Structural Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11269-022-03120-5.pdf

Reference36 articles.

1. Aichouri I, Hani A, Bougherira N, Djabri L, Chaffai H, Lallahem S (2015) River Flow Model Using ArtificialNeural Networks. Energy Procedia 74:1007–1014

2. Arias-Rodriguez L, Duan Z, Sepúlveda R, Martinez-Martinez S, Disse M (2020) Monitoring Water Quality ofValle de Bravo Reservoir, Mexico, Using Entire Lifespan ofMERIS Data and Machine Learning Approaches, Remotesensing, 12(10):1586–1611

3. Baek S, Pyo J, Chun J (2020) Prediction of Water Level and Water Quality Using a CNN-LSTM Combined Deep Learning Approach, Water, 12:3399

4. Barton D, Sundt H, Bustos A, Fjeldstad H, Hedger R, Forseth T, Köhler B, Aas Ø, Alfredsen K, Madsen A (2020) Multi-criteria decision analysis in Bayesian networks - Diagnosing ecosystem service trade-offs in a hydropower regulated river. Environ Model Softw 124:104604

5. Chen H, Chen A, Xu L, Xie H, Qiao H, Lin Q, Cai K (2020) A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources. Agric Water Manage 240(1):106303

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