Unstructured mesh-based graph neural networks for estimating the spatiotemporal distribution of a human-induced chemical in freshwater
Bibliographic record
Abstract
Artificial sweeteners such as acesulfame are anthropogenic contaminants increasingly detected in natural waters via wastewater effluents. Numerical models such as HydroGeoSphere (HGS) are widely used to simulate their spatiotemporal transport. However, high computational demands—especially when using unstructured meshes to capture complex geometries—limit their scalability for large-scale or long-term applications. To address this limitation, we developed a mesh-based graph neural network (Mesh-GNN), adapted from MeshGraphNets, to efficiently emulate HGS outputs over unstructured triangular meshes. The model was applied to the upper Grand River, Ontario, Canada, using topographical, geographical, hydrological, hydrometeorological, and wastewater point-source data to estimate acesulfame concentrations. Mesh-GNN retained the node and edge structure of the HGS mesh and enabled rapid inference via message passing. The model training yielded Nash-Sutcliffe Efficiency (NSE) values of 0.93 (spatial split) and 0.86 (temporal split), with corresponding validation NSEs of 0.69 and 0.70. Incorporating field observations with HGS-simulated concentrations improved accuracy at sampling sites by up to 29.7% compared to HGS alone. While HGS solves nonlinear partial differential equations across a three-dimensional watershed-scale mesh (∼3.5 million nodes), requiring several days per simulation, Mesh-GNN operates on a simplified two-dimensional upstream segment (5,755 nodes), enabling inference within seconds. These findings highlight the potential of Mesh-GNN-based surrogate models for efficient and scalable water quality prediction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".