FREE: The Foundational Semantic Recognition for Modeling Environmental Ecosystems
This addresses the problem of building general models for complex environmental relationships across space and time, which is incremental as it adapts existing semantic recognition techniques to environmental science.
The paper tackles the challenge of modeling environmental ecosystems by proposing FREE, a framework that maps environmental data into text space and converts predictive modeling into a semantic recognition problem, achieving superior performance over baselines in stream water temperature and crop yield prediction, even in data-sparse scenarios.
Modeling environmental ecosystems is critical for the sustainability of our planet, but is extremely challenging due to the complex underlying processes driven by interactions amongst a large number of physical variables. As many variables are difficult to measure at large scales, existing works often utilize a combination of observable features and locally available measurements or modeled values as input to build models for a specific study region and time period. This raises a fundamental question in advancing the modeling of environmental ecosystems: how to build a general framework for modeling the complex relationships among diverse environmental variables over space and time? In this paper, we introduce a framework, FREE, that enables the use of varying features and available information to train a universal model. The core idea is to map available environmental data into a text space and then convert the traditional predictive modeling task in environmental science to a semantic recognition problem. Our evaluation on two societally important real-world applications, stream water temperature prediction and crop yield prediction, demonstrates the superiority of FREE over multiple baselines, even in data-sparse scenarios.