Embedding ecological knowledge into artificial neural network training: A marine phytoplankton primary production model case study

Enhancing the understanding of marine phytoplankton primary production is paramount due to the relationships with oceanic food webs, energy fluxes, carbon cycle and Earth's climate. As field measurements of this process are both expensive and time consuming, indirect approaches, which can estimate primary production from remotely sensed imagery, are the only viable large-scale solution. We boosted the quality of phytoplankton primary production estimates, with respect to a previously developed model, by embedding ecological knowledge into the training of an artificial neural network. In order to achieve this goal, we drove the training procedure on the basis of both theoretical and data-derived ecological knowledge about phytoplankton primary production. A “single peak” constraint exploits the theoretical knowledge about the vertical shape of the production profile; a “depth-weighted error” procedure was based on available information about the production magnitude along the water column; a variable learning rate and momentum approach allowed to better exploit the available data to train our artificial neural network. Thanks to this customized procedure, we improved the quality of the primary production estimates from both a theoretical and a numerical point of view. Accordingly, the new artificial neural networks not only provided ecologically sounder estimates, but also explained up to 4% more variance with respect to the traditional error back-propagation solution. This result was achieved exclusively through an ecologically-driven customization of the basic algorithm, since the dataset and predictive variables were the same utilized in the conventional counterpart, trained with a classic error back-propagation algorithm. We suggest that the proposed rationale could lead to improved performances in similar modelling applications