Abstract
Monitoring the marine ecosystem can be done via observations (either in-situ or satellite) and via deterministic models. However, each of these methods has some drawbacks: observations can be accurate but insufficient in terms of temporal and spatial coverage, while deterministic models cover the whole marine ecosystem but can be inaccurate. This work aims at developing a deep learning model to reproduce the biogeochemical variables in the Mediterranean Sea, integrating observations and the output of an existing deterministic model of the marine ecosystem. In particular, two deep learning architectures will be proposed and tested: first EmuMed, an emulator of the deterministic model, and then InpMed, which consists of an improvement of the latter by the addition of information provided by in-situ and satellite observations. Results show that EmuMed can successfully reproduce the output of the deterministic model, while ImpMed can successfully make use of the additional information provided, thus improving our ability to monitor the biogeochemical variables in the Mediterranean Sea.
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Pietropolli, G., Cossarini, G., Manzoni, L. (2022). GANs for Integration of Deterministic Model and Observations in Marine Ecosystem. In: Marreiros, G., Martins, B., Paiva, A., Ribeiro, B., Sardinha, A. (eds) Progress in Artificial Intelligence. EPIA 2022. Lecture Notes in Computer Science(), vol 13566. Springer, Cham. https://doi.org/10.1007/978-3-031-16474-3_37
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DOI: https://doi.org/10.1007/978-3-031-16474-3_37
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