Reliable machine learning initialization methods for the calibration of Dynamic Energy Budget models

Dynamic Energy Budget (DEB) theory is a general theory that describes how organisms utilize the energy in food for maintenance, growth, development, and reproduction. DEB models have been widely applied in fields such as conservation biology, aquaculture and ecotoxicology, due to their ability to simulate how organisms respond to changing environmental conditions. To obtain a DEB model, the calibration problem must be solved: find the parameters that minimize the deviation between observed data and model predictions. While DEB model calibration is largely automated, the selection of initial parameters remains a key unresolved step, since the only automated method – the bijection method – often fails to produce a feasible initial parameter set. Consequently, modelers resort to trial-and-error to find parameters to seed the estimation. To bridge this gap, we propose using machine learning to initialize the calibration. We develop two models: a neural network and a 1-nearest-neighbor. Both models are built with a focus on feasibility, directly integrating parameter constraints into their structure. We train and evaluate our methods on the 5000+ DEB models in the Add-my-Pet database. Both methods generate feasible parameter sets in 99% of cases — compared to only 40% for the bijection method. The neural network initialization leads to improved DEB model calibration, achieving a calibration loss three times lower, on average, when compared to other methods. To support broader adoption, we have open-sourced our code and our models are available as initialization options within DEBtool , the primary software for parameter calibration. • Neural network approach integrates parameter constraints in model structure. • Nearest neighbor method automates choosing parameters of a similar species. • ML methods produce feasible parameter sets for 99% of species tested. • Improved initialization leads to a better model fit and fewer execution errors. • Open-source implementation available in the DEBtool model calibration software.