Original title: FREE: The Foundational Semantic Recognition for Modeling Environmental Ecosystems
Authors: Shiyuan Luo, Juntong Ni, Shengyu Chen, Runlong Yu, Yiqun Xie, Licheng Liu, Zhenong Jin, Huaxiu Yao, Xiaowei Jia
This article delves into the challenge of modeling environmental ecosystems, highlighting the complexity due to numerous interacting variables. Current approaches rely on limited data or modeled values, prompting the need for a universal framework. Enter FREE—a novel approach transforming environmental data into a text space and reframing prediction as a semantic recognition task. By leveraging language models, FREE integrates natural language descriptions with existing features, capturing data nuances and accommodating irregularities. This framework, evaluated across real-world scenarios like predicting stream water temperature and corn yield, demonstrates superior predictive accuracy compared to baseline methods. Furthermore, FREE stands out for its efficiency, enabling pre-training on simulated data and adapting to incorporate new observations for enhanced future predictions. This innovation not only improves predictive performance but also streamlines computational resources, advancing environmental modeling for sustainable solutions.
Original article: https://arxiv.org/abs/2311.10255