Employing hybrid deep learning for near-real-time forecasts of sensor-based algal parameters in a Microcystis bloom-dominated lake-Reference-Cited by-同舟云学术

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Employing hybrid deep learning for near-real-time forecasts of sensor-based algal parameters in a Microcystis bloom-dominated lake

Published:2024-04 Issue: Volume:922 Page:171009
ISSN:0048-9697
Container-title:Science of The Total Environment
language:en
Short-container-title:Science of The Total Environment

Author:

Wang Lan,Shan Kun,Yi Yang,Yang Hong,Zhang Yanyan,Xie Mingjiang,Zhou Qichao,Shang Mingsheng

Funder

Science and Technology Department of Yunnan Province

West Light Foundation of the Chinese Academy of Sciences

Chongqing Municipal Education Commission Foundation

National Natural Science Foundation of China

Chongqing Science and Technology Commission

Publisher

Elsevier BV

Reference67 articles.

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

2. Predicting Cyanobacterial Harmful Algal Blooms (CyanoHABs) in a regulated river using a revised EFDC model;Ahn;Water,2021

3. Short-term Lake Erie algal bloom prediction by classification and regression models;Ai;Water Res.,2023

4. Machine learning for anomaly detection in cyanobacterial fluorescence signals;Almuhtaram;Water Res.,2021

5. Artificial intelligence modelling integrated with Singular Spectral analysis and Seasonal-Trend decomposition using Loess approaches for streamflow predictions;Apaydin;J. Hydrol.,2021

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. Enhanced forecasting of chlorophyll-a concentration in coastal waters through integration of Fourier analysis and Transformer networks;Water Research;2024-10

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