Modelling Biometeorological Processes using Physics-Informed Neural Networks

Biometeorological models have traditionally been categorized as mechanistic (deterministic) or stochastic, with recent expansions to include machine learning (ML) models. Mechanistic models represent system processes based on a cause-effect concept and domain knowledge, while a subset—physics-based models—explicitly incorporate physical laws. Despite their interpretability, such models often rely on empirically fixed parameters and may overlook complex environmental interactions. In this study, we investigate a hybrid modeling framework that combines physics-based modeling with Physics-Informed Neural Networks (PINNs) to enhance the simulation of biosphere-atmosphere interactions.

Focusing on mosquito population dynamics as a climate-sensitive system, we couple a physics-based dynamic model with a PINN to improve representation of environmental drivers affecting larval and pupal development rates. Traditionally, air temperature is used as the primary forcing variable in such models. However, our results show that the PINN, trained on historical meteorological and entomological data, identifies precipitation and humidity as significant additional predictors of mosquito development dynamics. This enriched modeling captures population peaks more accurately and improves predictive performance during critical seasonal transitions.

By integrating physics-based structure with data-driven learning, the hybrid model maintains explainability while revealing hidden nonlinear dependencies among meteorological variables. The findings demonstrate how advanced ML techniques like PINNs can uncover meteorological sensitivities that traditional models may not capture—highlighting the importance of meteorological data in biosphere modeling.

This approach not only enhances disease vector modeling under varying climate conditions but also offers a transferable framework for other environmental applications such as crop phenology, urban microclimate analysis, and infectious diseases transmission in the human population. The study underscores the value of combining physics-based models with machine learning to extract deeper insight from complex meteorological data. 

Acknowledgements: This research is supported by the Ministry of Science, Technological Development and Innovation of the Republic of Serbia (Grants No. ‪451-03-137/2025-03/ 200125 & 451-03-136/2025-03/ 200125) and COST Action CA20108 FAIR Network of micrometeorological measurements (FAIRNESS).