Model Generalisation for Predicting the Amount of Photosynthetically Available Radiation in the Water Column from Freefall Profiler Observations
DOI:
https://doi.org/10.26034/lu.akwi.2024.6224Keywords:
Machine Learning, Underwater Light Field, Photosynthetic Active Radiation, Freefall Profiler, KNIMEAbstract
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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Copyright (c) 2024 Christoph Tholen, Lars Nolle, Jochen Wollschläger, Frederic Stahl
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