Forecasting coastal hypoxia using a blend of mechanistic and artificial intelligence models

Daily fluctuations in coastal hypoxia significantly impact marine ecosystems, requiring forecasts that balance efficiency and accuracy. Statistical models are computationally efficient but often fall short in prediction performance, while mechanistic models are accurate yet resource-intensive. Here,...

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Vydané v:	Scientific reports Ročník 15; číslo 1; s. 31452 - 18
Hlavní autori:	Ou, Yanda, Xue, Z. George, Mukhopadhyay, Supratik, Rajasekaran, Magesh, Wichman, Dylan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Nature Publishing Group UK 26.08.2025 Nature Publishing Group Nature Portfolio
Predmet:	704/47 704/829 704/829/2737 Artificial intelligence Biogeochemistry Business metrics Coastal zone management Decision making DeepLabv3 Fish Fluid mechanics Forecasting Humanities and Social Sciences Hypoxia Hypoxia forecast Louisiana-Texas shelf Marine ecosystems multidisciplinary Nutrient loading Science Science (multidisciplinary) Statistical models U-Net Water column Water quality Water stratification Louisiana United States > US Texas Hypoxia forecast DeepLabv3 Artificial intelligence U-Net Louisiana-Texas shelf
ISSN:	2045-2322, 2045-2322
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Popis
Shrnutí:	Daily fluctuations in coastal hypoxia significantly impact marine ecosystems, requiring forecasts that balance efficiency and accuracy. Statistical models are computationally efficient but often fall short in prediction performance, while mechanistic models are accurate yet resource-intensive. Here, we present a lightweight artificial intelligence (AI) model for daily hypoxia forecasting on the Louisiana–Texas shelf, trained and validated using a 14-year mechanistic ROMS hindcast. The AI model integrates observed riverine nutrient loads and 2-day hydrodynamic forecasts and achieves strong predictive performance: median (± 1 s.d.) accuracy of 0.85 ± 0.07 and F1 score of 0.72 ± 0.18 against the hindcast test set, and 0.67 ± 0.10 accuracy with 0.62 ± 0.14 F1 score against shelf-wide cruise observations. The model remains robust when applied to independent hydrodynamic forecasts (accuracy = 0.71 ± 0.09; F1 score = 0.64 ± 0.17). Beyond forecasting, the AI model enables rapid scenario testing for coastal management. Nutrient reduction assessments suggest that reductions exceeding 90% may be required to meet Gulf Hypoxia Task Force goals. Ablation experiments identify water column stratification as the dominant predictor of daily hypoxia events. This study demonstrates the potential of AI to enhance real-time water quality forecasting, support management decision-making, and inform adaptive cruise planning in dynamic coastal systems.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2045-2322 2045-2322
DOI:	10.1038/s41598-025-17053-7