2023/11/17 by Luo, Shiyuan, Ni, Juntong, Chen, Shengyu +6 · 1 citation
#FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Populations and Evolution (q-bio.PE)
paper · doi:10.48550/arxiv.2311.10255
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.