Geo-Aware Models for Stream Temperature Prediction across Different Spatial Regions and Scales
This addresses the challenge of scalable, data-efficient environmental monitoring for water quality management, though it appears incremental as it builds on existing spatio-temporal graph neural networks with a novel embedding.
The paper tackled the problem of predicting stream water temperature across varying spatial regions and scales, where existing models struggle due to data heterogeneity and limited samples, and proposed Geo-STARS, a geo-aware spatio-temporal framework that outperformed state-of-the-art baselines on real-world datasets spanning 37 years across multiple watersheds.
Understanding environmental ecosystems is vital for the sustainable management of our planet. However,existing physics-based and data-driven models often fail to generalize to varying spatial regions and scales due to the inherent data heterogeneity presented in real environmental ecosystems. This generalization issue is further exacerbated by the limited observation samples available for model training. To address these issues, we propose Geo-STARS, a geo-aware spatio-temporal modeling framework for predicting stream water temperature across different watersheds and spatial scales. The major innovation of Geo-STARS is the introduction of geo-aware embedding, which leverages geographic information to explicitly capture shared principles and patterns across spatial regions and scales. We further integrate the geo-aware embedding into a gated spatio-temporal graph neural network. This design enables the model to learn complex spatial and temporal patterns guided by geographic and hydrological context, even with sparse or no observational data. We evaluate Geo-STARS's efficacy in predicting stream water temperature, which is a master factor for water quality. Using real-world datasets spanning 37 years across multiple watersheds along the eastern coast of the United States, Geo-STARS demonstrates its superior generalization performance across both regions and scales, outperforming state-of-the-art baselines. These results highlight the promise of Geo-STARS for scalable, data-efficient environmental monitoring and decision-making.