Towards detailed and interpretable hybrid modeling of continental-scale bird migration
This work addresses limitations in ecological interpretability for researchers studying bird migration patterns, representing an incremental improvement to an existing hybrid modeling approach.
The authors tackled the problem of improving spatial resolution and interpretability in hybrid models of continental-scale bird migration by modifying the FluxRGNN model to work on arbitrary spatial tessellations and adding explicit incentives for predicting take-off/landing events, resulting in strong extrapolation capabilities to unobserved locations in experiments on the U.S. weather radar network.
Hybrid modeling aims to augment traditional theory-driven models with machine learning components that learn unknown parameters, sub-models or correction terms from data. In this work, we build on FluxRGNN, a recently developed hybrid model of continental-scale bird migration, which combines a movement model inspired by fluid dynamics with recurrent neural networks that capture the complex decision-making processes of birds. While FluxRGNN has been shown to successfully predict key migration patterns, its spatial resolution is constrained by the typically sparse observations obtained from weather radars. Additionally, its trainable components lack explicit incentives to adequately predict take-off and landing events. Both aspects limit our ability to interpret model results ecologically. To address this, we propose two major modifications that allow for more detailed predictions on any desired tessellation while providing control over the interpretability of model components. In experiments on the U.S. weather radar network, the enhanced model effectively leverages the underlying movement model, resulting in strong extrapolation capabilities to unobserved locations.