Title:
Model generalisation for predicting the amount of photosynthetically available radiation in the water column from freefall profiler observations
Authors:
- Christoph Tholen
- Lars Nolle
- Jochen Wollschlaeger
- Frederic Stahl
Published in:
(2024). ECMS 2024, 38th Proceedings
Edited by: Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev, European Council for Modelling and Simulation.
DOI: http://doi.org/10.7148/2024
ISSN: 2522-2422 (ONLINE)
ISSN: 2522-2414 (PRINT)
ISSN: 2522-2430 (CD-ROM)
ISBN: 978-3-937436-84-5
ISBN: 978-3-937436-83-8 (CD) Communications of the ECMS Volume 38, Issue 1, June 2024, Cracow, Poland June 4th – June 7th, 2024
DOI:
https://doi.org/10.7148/2024-0381
Citation format:
Christoph tholen, Lars nolle, Jochen wollschlaeger, Frederic stahl (2024). Model Generalisation for Predicting the Amount of Photosynthetically Available Radiation in the Water Column from Freefall Profiler Observations, ECMS 2024, Proceedings Edited by: Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev, European Council for Modelling and Simulation. doi:10.7148/2024-0381
Abstract:
In modern oceanography Photosynthetically Available Radiation (PAR) is used for modelling vegetation growth as it is a requirement for the process of photosynthesis. PAR as integrated value of the light spectrum between 400-700 nm can be measured directly using respective sensor systems. However, PAR can also be determined indirectly using measurements from only a small number of discrete wavelengths. In this paper, such a modelling approach is presented for predicting PAR in the water column. The approach uses spectral information within the water column and from above the sea surface. Three different modelling approaches based on artificial intelligence (AI) were used. It was shown that the artificial neural network (ANN) approach outperformed the regression tree (RT) and the linear regression (LR) approaches. It was also shown that the models generalise well, with an accuracy loss of 10 % based on the median, on data recorded in other geolocations without additional modification or re-training.
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