| DOI | Trouver le DOI : https://doi.org/10.1109/IJCNN60899.2024.10651143 |
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| Auteur | Rechercher : Durand, Guillaume1; Rechercher : Valdés, Julio J.1Identifiant ORCID : https://orcid.org/0000-0003-2930-0325; Rechercher : Guyondet, Thomas; Rechercher : Rice, Olivia Gerry; Rechercher : Coffin, Michael |
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| Affiliation | - Conseil national de recherches du Canada. Technologies numériques
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| Bailleur de fonds | Rechercher : Environment and Climate Change Canada; Rechercher : National Research Council Canada; Rechercher : Canadian Food Inspection Agency; Rechercher : Canadian Space Agency |
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| Format | Texte, Article |
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| Conférence | 2024 International Joint Conference on Neural Networks (IJCNN), June 30 - July 5, 2024, Yokohama, Japan |
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| Sujet | estuary ecosystems; dissolved oxygen time series; non-linear dynamic systems; chaos theory; modeling; machine learning; chaos; analytical models; fluctuations; computational modeling; biological system modeling; time series analysis; predictive models |
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| Résumé | This paper introduces a novel approach for modeling dissolved oxygen (DO) time series data collected in Atlantic Canada. The primary objective is to propose a solution that can predict significant fluctuations in dissolved oxygen levels in estuaries, thereby improving our ability to manage and protect these dynamic ecosystems. Dissolved oxygen serves as a crucial indicator of water quality and is influenced by various factors such as temperature, nutrient concentrations, and biological activities, resulting in a nonlinear dynamic system. By analyzing chaos-related metrics, we demonstrate that the inherent unpredictability and sensitivity of DO time series can be effectively captured. We show that the modeling of the magnitude and duration of DO fluctuations can be anticipated by studying features like bifurcation patterns. Our research effectively characterizes dissolved oxygen series as nonlinear dynamic systems, revealing the internal structure of these systems and enabling differentiation of sites based on their hypoxic behavior. We develop models using both classical and deep learning-based early warning/tipping point indicators, which successfully predict the occurrence of hypoxic events even in degraded sites. The presented results highlight the advantages of our proposed method in anticipating tipping points and predicting the duration of anoxic episodes, particularly in highly degraded sites. |
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| Date de publication | 2024-06-30 |
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| Maison d’édition | IEEE |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 7d255869-22c2-4497-b5d3-aba20a685a6a |
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| Enregistrement créé | 2024-09-11 |
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| Enregistrement modifié | 2024-09-12 |
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