Volume 56, pp. 138-156, 2022.

Approximation of a marine ecosystem model by artificial neural networks

Markus Pfeil and Thomas Slawig


Marine ecosystem models are important to identify the processes that affect for example the global carbon cycle. Computation of an annually periodic solution (i.e., a steady annual cycle) for these models requires a high computational effort. To reduce this effort, we approximate an exemplary marine ecosystem model by different artificial neural networks (ANNs). We use a fully connected network (FCN), then apply the sparse evolutionary training (SET) procedure, and finally apply a genetic algorithm (GA) to optimize, inter alia, the network topology. With all three approaches, a direct approximation of the steady annual cycle is not sufficiently accurate. However, using the mass-corrected prediction of the ANN as initial concentration for additional model runs, the results are in very good agreement. In this way, we achieve a runtime reduction by about 15%. The results from the SET algorithm are comparable to those of the FCN. Further application of the GA may lead to an even higher reduction.

Full Text (PDF) [8.5 MB], BibTeX

Key words

deep learning, genetic algorithm, sparse evolutionary training, biogeochemical modeling, marine ecosystem modeling, transport matrix method

AMS subject classifications

68T07, 68U99, 92F05

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