| DOI | Resolve DOI: https://doi.org/10.1109/OCEANS55160.2024.10754209 |
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| Author | Search for: Durand, Guillaume1; Search for: Valdés, Julio J.1ORCID identifier: https://orcid.org/0000-0003-2930-0325; Search for: Guyondet, Thomas; Search for: Rice, Olivia Gerry; Search for: Coffin, Michael |
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| Affiliation | - National Research Council Canada. Digital Technologies
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| Funder | Search for: Environment and Climate Change Canada; Search for: National Research Council Canada; Search for: Canadian Food Inspection Agency; Search for: Canadian Space Agency |
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| Format | Text, Article |
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| Conference | OCEANS 2024, September 23-26, 2024, Halifax, NS, Canada |
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| Subject | chaos; correlation; oceans; time series analysis; estimation; machine learning; predictive models; estuaries; rivers; manifold learning |
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| Abstract | This paper presents and experiments with a new validation dataset using a method that models anoxia in estuaries. The proposed method incorporates nonlinear time series characterization, time delay methods, unsupervised techniques for intrinsic dimensionality estimation, and manifold learning to predict anoxic events. Characterizing dissolved oxygen time series with features derived from nonlinear time series analysis effectively identified families of series exhibiting differences in their dynamics. The built models showed high correlations and relatively low errors, thus validating the approach on these additional data. However, further work is needed to develop more advanced models capable of operating with time-ordered events. |
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| Publication date | 2024-09-23 |
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| Publisher | IEEE |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 3e047595-38d7-45df-8a4c-8af9c2b3ee15 |
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| Record created | 2024-12-02 |
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| Record modified | 2025-11-03 |
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