FREE: The Foundational Semantic Recognition for Modeling Environmental
  Ecosystems
Shiyuan Luo Juntong Ni Shengyu Chen Runlong Yu Yiqun Xie Licheng Liu Zhenong Jin Huaxiu Yao Xiaowei Jia
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 amongst various environmental data over space and time?
In this paper, we introduce a new framework, FREE, which maps available
environmental data into a text space and then converts the traditional
predictive modeling task in environmental science to the semantic recognition
problem. The proposed FREE framework leverages recent advances in Large
Language Models (LLMs) to supplement the original input features with natural
language descriptions. This facilitates capturing the data semantics and also
allows harnessing the irregularities of input features. When used for long-term
prediction, FREE has the flexibility to incorporate newly collected
observations to enhance future prediction. The efficacy of FREE is evaluated in
the context of two societally important real-world applications, predicting
stream water temperature in the Delaware River Basin and predicting annual corn
yield in Illinois and Iowa. Beyond the superior predictive performance over
multiple baseline methods, FREE is shown to be more data- and
computation-efficient as it can be pre-trained on simulated data generated by
physics-based models.