Prediction of Total Phosphorus Concentration in Canals by GAT-Informer Model Based on Spatiotemporal Correlations DOI Open Access
Juan Huan, Xincheng Li,

Jialong Yuan

et al.

Water, Journal Year: 2024, Volume and Issue: 17(1), P. 12 - 12

Published: Dec. 24, 2024

The accurate prediction of total phosphorus (TP) is crucial for the early detection water quality eutrophication. However, predicting TP concentrations among canal sites challenging due to their complex spatiotemporal dependencies. To address this issue, study proposes a GAT-Informer method based on correlations predict in Beijing–Hangzhou Grand Canal Basin Changzhou City. begins by creating feature sequences each site time lag relationship concentration between sites. It then constructs graph data combining real river distance and correlation sequences. Next, spatial features are extracted fusing node using attention (GAT) module. employs Informer network, which uses sparse mechanism extract temporal efficiently simulating model was evaluated R2, MAE, RMSE, with experimental results yielding values 0.9619, 0.1489%, 0.1999%, respectively. exhibits enhanced robustness superior predictive accuracy comparison traditional models.

Language: Английский

Machine learning-driven predictive frameworks for optimizing chemical strategies in Microcystis aeruginosa mitigation DOI

Zobia Khatoon,

Suiliang Huang,

Adeel Ahmed Abbasi

et al.

Journal of Water Process Engineering, Journal Year: 2025, Volume and Issue: 71, P. 107235 - 107235

Published: Feb. 12, 2025

Language: Английский

Citations

0
Comparing neural network architectures for simulating pollutant loads and first flush events in urban watersheds: Balancing specialization and generalization DOI
Angela Gorgoglione, Cosimo Russo, Andrea Gioia

et al.

Chemosphere, Journal Year: 2025, Volume and Issue: 379, P. 144395 - 144395

Published: April 24, 2025

Language: Английский

Citations

0
Prediction of Total Phosphorus Concentration in Canals by GAT-Informer Model Based on Spatiotemporal Correlations DOI Open Access
Juan Huan, Xincheng Li,

Jialong Yuan

et al.

Water, Journal Year: 2024, Volume and Issue: 17(1), P. 12 - 12

Published: Dec. 24, 2024

The accurate prediction of total phosphorus (TP) is crucial for the early detection water quality eutrophication. However, predicting TP concentrations among canal sites challenging due to their complex spatiotemporal dependencies. To address this issue, study proposes a GAT-Informer method based on correlations predict in Beijing–Hangzhou Grand Canal Basin Changzhou City. begins by creating feature sequences each site time lag relationship concentration between sites. It then constructs graph data combining real river distance and correlation sequences. Next, spatial features are extracted fusing node using attention (GAT) module. employs Informer network, which uses sparse mechanism extract temporal efficiently simulating model was evaluated R2, MAE, RMSE, with experimental results yielding values 0.9619, 0.1489%, 0.1999%, respectively. exhibits enhanced robustness superior predictive accuracy comparison traditional models.

Language: Английский

Citations

1