Enhanced water quality prediction by LSTM and graph attention network (L-GAT): An analytical study of the Pearl River Basin
Accurate water quality prediction plays a pivotal role in watershed management, yet it remains challenging due to the nonlinearity, non-stationarity, and multi-source variability of river systems. To address this, we propose L-GAT, a novel spatiotemporal forecasting approach that integrates Graph At...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Water Research X |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589914725000829 |
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