LakeFM: Toward a Foundation Model for Aquatic Ecosystems Using Irregular Multivariate Multi-depth Time Series Data

Authors:Abhilash Neog, Sepideh Fatemi, Medha Sawhney, Kazi Sajeed Mehrab, Aanish Pradhan, Bennett J. McAfee, Emma Marchisin, Arka Daw, Robert Ladwig, Cayelan C. Carey, Paul Hanson, Anuj Karpatne

Abstract:Understanding and forecasting lake dynamics is critical for monitoring water quality and ecosystem health across lakes and reservoirs. While machine learning methods have been recently applied to ecological time-series data, existing works assume regular sampling in time and depth, and struggle to generalize across lakes with heterogeneous variables, depths, and observation patterns. To address these limitations, we introduce \textsc{LakeFM}, a foundation model for aquatic systems, pre-trained on large-scale ecological datasets comprising both simulated and observed lakes. Through extensive empirical evaluation, we show that \textsc{LakeFM} learns meaningful representations spanning broader lake-level characteristics, and achieves competitive or often superior-forecasting performance compared to existing time-series foundation and non-foundation models, while producing physically plausible predictions consistent with real-world lake dynamics.

Machine Learning (cs.LG)

arXiv:2606.11268 [cs.LG]

(or arXiv:2606.11268v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.11268 arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.1145/3770855.3819024 DOI(s) linking to related resources