Forecasting hypoxia events in north Atlantic ecosystems using chaotic dynamics
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 prote...
Published in: | 2024 International Joint Conference on Neural Networks (IJCNN) |
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ftnrccanada:oai:cisti-icist.nrc-cnrc.ca:cistinparc:7d255869-22c2-4497-b5d3-aba20a685a6a 2024-09-30T14:39:40+00:00 Forecasting hypoxia events in north Atlantic ecosystems using chaotic dynamics Durand, Guillaume Valdés, Julio J. Guyondet, Thomas Rice, Olivia Gerry Coffin, Michael 2024-06-30 text https://doi.org/10.1109/IJCNN60899.2024.10651143 https://nrc-publications.canada.ca/eng/view/object/?id=7d255869-22c2-4497-b5d3-aba20a685a6a https://nrc-publications.canada.ca/fra/voir/objet/?id=7d255869-22c2-4497-b5d3-aba20a685a6a eng eng IEEE 2024 International Joint Conference on Neural Networks (IJCNN), 2024 International Joint Conference on Neural Networks (IJCNN), June 30 - July 5, 2024, Yokohama, Japan, ISBN: 979-8-3503-5931-2, Publication date: 2024-06-30 doi:10.1109/IJCNN60899.2024.10651143 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 article 2024 ftnrccanada https://doi.org/10.1109/IJCNN60899.2024.10651143 2024-09-17T00:02:19Z 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. Peer reviewed: Yes NRC publication: Yes Article in Journal/Newspaper North Atlantic National Research Council Canada: NRC Publications Archive Canada 2024 International Joint Conference on Neural Networks (IJCNN) 1 8 |
institution |
Open Polar |
collection |
National Research Council Canada: NRC Publications Archive |
op_collection_id |
ftnrccanada |
language |
English |
topic |
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 |
spellingShingle |
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 Durand, Guillaume Valdés, Julio J. Guyondet, Thomas Rice, Olivia Gerry Coffin, Michael Forecasting hypoxia events in north Atlantic ecosystems using chaotic dynamics |
topic_facet |
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 |
description |
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. Peer reviewed: Yes NRC publication: Yes |
format |
Article in Journal/Newspaper |
author |
Durand, Guillaume Valdés, Julio J. Guyondet, Thomas Rice, Olivia Gerry Coffin, Michael |
author_facet |
Durand, Guillaume Valdés, Julio J. Guyondet, Thomas Rice, Olivia Gerry Coffin, Michael |
author_sort |
Durand, Guillaume |
title |
Forecasting hypoxia events in north Atlantic ecosystems using chaotic dynamics |
title_short |
Forecasting hypoxia events in north Atlantic ecosystems using chaotic dynamics |
title_full |
Forecasting hypoxia events in north Atlantic ecosystems using chaotic dynamics |
title_fullStr |
Forecasting hypoxia events in north Atlantic ecosystems using chaotic dynamics |
title_full_unstemmed |
Forecasting hypoxia events in north Atlantic ecosystems using chaotic dynamics |
title_sort |
forecasting hypoxia events in north atlantic ecosystems using chaotic dynamics |
publisher |
IEEE |
publishDate |
2024 |
url |
https://doi.org/10.1109/IJCNN60899.2024.10651143 https://nrc-publications.canada.ca/eng/view/object/?id=7d255869-22c2-4497-b5d3-aba20a685a6a https://nrc-publications.canada.ca/fra/voir/objet/?id=7d255869-22c2-4497-b5d3-aba20a685a6a |
geographic |
Canada |
geographic_facet |
Canada |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_relation |
2024 International Joint Conference on Neural Networks (IJCNN), 2024 International Joint Conference on Neural Networks (IJCNN), June 30 - July 5, 2024, Yokohama, Japan, ISBN: 979-8-3503-5931-2, Publication date: 2024-06-30 doi:10.1109/IJCNN60899.2024.10651143 |
op_doi |
https://doi.org/10.1109/IJCNN60899.2024.10651143 |
container_title |
2024 International Joint Conference on Neural Networks (IJCNN) |
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1 |
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8 |
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1811642283236261888 |