Evaluating Transferability and Robustness of Process-Guided Neural Networks in Forest Carbon Flux Modelling

Process-guided neural networks (PGNNs) improve forest carbon flux prediction over both stand-alone PRELES and data-driven ANNs, especially in data-sparse and climate-transfer scenarios.

• Why it matters: Limited forest data and slow environmental change make robust, generalizable ecological forecasting difficult, and prior PGNN designs need testing for transferability and robustness.
• What they did: The study integrates PRELES with deep learning, replicates and extends prior PGNN work using new model types, and runs nested cross-validation under data-thinning and extrapolative transfer scenarios.
• The result: Both ANN and PGNN outperform PRELES, while PGNNs add advantages in data-sparse and unseen changing-climate conditions; generalization error is estimated versus training-data amount, and variable importance shows physically plausible learning by PGNNs/ANNs but strong boreal bias in PRELES.

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