RITESH MOON
Ritesh is a PhD researcher working at the intersection of hydrology and machine learning, developing physically informed machine learning (PIML) frameworks that integrate process-based hydrological models with deep learning to improve streamflow prediction in complex and regulated catchments. Using large-sample datasets such as CAMELS-GB, he incorporates hydrological signatures and process-based insights to enhance both predictive accuracy and model interpretability. His work focuses on bridging physics-based understanding with modern AI to build robust, scalable, and transparent tools for water resource management.
Session
Scientific modeling often forces a choice between flexible but opaque neural networks and interpretable process-based models that can be too rigid for real-world data. Hybrid modeling bridges this gap by combining mechanistic structure with machine-learning flexibility. In this talk we introduce EasyHybrid.jl, a user-friendly Julia package that makes hybrid modeling accessible across disciplines. We demonstrate the approach on a concrete problem: partitioning eddy-covariance net carbon fluxes into photosynthesis (a CO₂ sink) and ecosystem respiration (a CO₂ source), while estimating how strongly respiration responds to temperature. Temperature sensitivity is summarized by Q10, the factor by which respiration changes for a 10 K warming (e.g., Q10 = 2 means doubles per 10 K). We present cross-site results across hundreds of FLUXNET eddy covariance towers and show that, even when inferred jointly with hybrid flux partitioning, the learned temperature sensitivity exhibits a relatively narrow convergence across ecosystems.