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Skákala, J, Awty-Carroll, K, Menon, PP, Wang, K and Lessin, G 2023 Future digital twins: emulating a highly complex marine biogeochemical model with machine learning to predict hypoxia. Frontiers in Marine Science, 10. https://doi.org/10.3389/fmars.2023.1058837
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Abstract/Summary
The Machine learning (ML) revolution is becoming established in oceanographic research, but its applications to emulate marine biogeochemical models are still rare. We pioneer a novel application of machine learning to emulate a highly complex physical-biogeochemical model to predict marine oxygen in the shelfsea environment. The emulators are developed with intention of supporting future digital twins for two key stakeholder applications: (i) prediction of hypoxia for aquaculture and fisheries, (ii) extrapolation of oxygen from marine observations. We identify the key drivers behind oxygen concentrations and determine the constrains on observational data for a skilled prediction of marine oxygen across the whole water column. Through this we demonstrate that ML models can be very useful in informing observation measurement arrays. We compare the performance of multiple different ML models, discuss the benefits of the used approaches and identify outstanding issues, such as limitations imposed by the spatio-temporal resolution of the training/validation data.
| Item Type: | Publication - Article |
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| Additional Keywords: | digital twins, machine learning emulator, oxygen prediction, shelf seas, marine biogeochemical model |
| Divisions: | Plymouth Marine Laboratory > National Capability categories > Single Centre NC - CLASS Plymouth Marine Laboratory > National Capability categories > Western Channel Observatory Plymouth Marine Laboratory > Science Areas > Marine System Modelling |
| Depositing User: | S Hawkins |
| Date made live: | 10 May 2023 14:51 |
| Last Modified: | 19 Jan 2024 10:51 |
| URI: | https://plymsea.ac.uk/id/eprint/9917 |
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