Prediction of chlorophyll‐a data based on triple‐stage attention recurrent neural network-Reference-Cited by-同舟云学术

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Prediction of chlorophyll‐a data based on triple‐stage attention recurrent neural network

Published:2022-12-16 Issue: Volume: Page:
ISSN:1751-8628
Container-title:IET Communications
language:en
Short-container-title:IET Communications

Author:

Chang Wenqing¹^ORCID,Li Xiang¹,Chaudhary Vikas²,Dong Huomin³,Zhao Zhigang¹,Nguyen Tri Gia⁴^ORCID

Affiliation:

1. Qilu University of Technology (Shandong Academy of Sciences) Shandong Computer Science Center (National Supercomputer Center in Jinan) Jinan China

2. JIMS Engineering Management Technical Campus, Greater Noida Greater Noida India

3. Shandong Kechuang Group Co. LTD Jinan China

4. FPT University Danang Vietnam

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Science Applications

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/cmu2.12542

Reference23 articles.

1. Red tide time series forecasting by combining ARIMA and deep belief network

2. Climate Change and Harmful Algal Blooms: Insights and perspective

3. Machine learning predictions of chlorophyll-a in the Han river basin, Korea

4. Estimating Coastal Chlorophyll-A Concentration from Time-Series OLCI Data Based on Machine Learning

5. Long-term prediction of sea surface chlorophyll-a concentration based on the combination of spatio-temporal features

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1. Time Series Representation Learning: A Survey on Deep Learning Techniques for Time Series Forecasting;Lecture Notes in Computer Science;2024

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