A hierarchical transformer and graph neural network model for high-accuracy watershed nitrate prediction.

ABSTRACT: Non-point source pollution from agricultural activities poses a significant threat to water quality by introducing excess nutrients like nitrogen into aquatic ecosystems, leading to issues such as eutrophication and groundwater contamination. In agricultural watersheds, nitrate transport involves intricate physical, chemical, and biological processes influenced by meteorological conditions, hydrological features, and spatial topologies, making accurate short-term predictions challenging. Traditional data-driven deep learning models often fail to incorporate physical constraints and complex spatiotemporal dynamics, limiting their interpretability and predictive accuracy. Here we show a hierarchical transformer and graph neural network model that accurately predicts watershed nitrate concent

SUBMITTER: Sun J 

PROVIDER: S-EPMC12639451 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature