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Modeling environmental ecosystems is critical for the sustainability of ourplanet, but is extremely challenging due to the complex underlying processesdriven by interactions amongst a large number of physical variables. As manyvariables are difficult to measure at large scales, existing works oftenutilize a combination of observable features and locally available measurementsor modeled values as input to build models for a specific study region and timeperiod. This raises a fundamental question in advancing the modeling ofenvironmental ecosystems: how to build a general framework for modeling thecomplex relationships amongst various environmental data over space and time?In this paper, we introduce a new framework, FREE, which maps availableenvironmental data into a text space and then converts the traditionalpredictive modeling task in environmental science to the semantic recognitionproblem. The proposed FREE framework leverages recent advances in LargeLanguage Models (LLMs) to supplement the original input features with naturallanguage descriptions. This facilitates capturing the data semantics and alsoallows harnessing the irregularities of input features. When used for long-termprediction, FREE has the flexibility to incorporate newly collectedobservations to enhance future prediction. The efficacy of FREE is evaluated inthe context of two societally important real-world applications, predictingstream water temperature in the Delaware River Basin and predicting annual cornyield in Illinois and Iowa. Beyond the superior predictive performance overmultiple baseline methods, FREE is shown to be more data- andcomputation-efficient as it can be pre-trained on simulated data generated byphysics-based models.

FREE: The Foundational Semantic Recognition for Modeling Environmental Ecosystems