构建首个湖泊生态系统基础模型，可处理不规则多深度时间序列数据。
LakeFM: Toward a Foundation Model for Aquatic Ecosystems Using Irregular Multivariate Multi-depth Time Series Data
- 基于大规模模拟与实测数据预训练，支持跨湖泛化
- 在多种湖泊变量预测上表现优于现有模型
- 适合水生态监测与气候变化研究者使用
理解与预测湖泊动态对监测水质与生态系统健康至关重要。尽管机器学习已应用于生态时间序列数据，但现有方法通常假设时间和深度采样规则，难以在变量、深度和观测模式异质的湖泊间泛化。为此，我们提出 extsc{LakeFM}——一个面向水生系统的基础模型，其在包含模拟与实测湖泊的大规模生态数据集上进行预训练。通过大量实证评估，我们发现 extsc{LakeFM} 能学习涵盖更广泛湖泊特征的有意义表征，在多种变量预测任务中达到或超过现有时间序列基础与非基础模型的表现，且预测结果符合真实湖泊动力学，具有物理合理性。
原文摘要 · Abstract (English)
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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