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

• URL: http://arxiv.org/abs/2606.11268v1
• Date: Tue, 09 Jun 2026 05:30:53 GMT
• Title: LakeFM: Toward a Foundation Model for Aquatic Ecosystems Using Irregular Multivariate Multi-depth Time Series Data
• Abstract summary: textscLakeFM is a foundation model for aquatic systems pre-trained on large-scale ecological datasets.<n>We show that textscLakeFM learns meaningful representations spanning broader lake-level characteristics.<n>It produces physically plausible predictions consistent with real-world lake dynamics.
• Score: 8.269068577570511
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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