Multi-station water level forecasting using advanced graph convolutional networks with adversarial learning

Multi-station water level forecasting using advanced graph convolutional networks with adversarial learning

This paper presents an advanced graph convolutional network model, enhanced with Wasserstein distance-based adversarial learning (WD-ACGN), addressing the limitations of existing single-station and less explored multi-station water level forecasting approaches. The model features a novel coupled mod...

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Bibliographic Details
Main Authors:	Xinhai Han, Xiaohui Li, Jingsong Yang, Jiuke Wang, Guoqi Han, Jun Ding, Hui Shen, Jun Yan, Dake Chen
Format:	Article
Language:	English
Published:	Taylor & Francis Group 2025-02-01
Series:	Geo-spatial Information Science
Subjects:	Water level multi-station forecast deep learning graph convolutional networks
Online Access:	https://www.tandfonline.com/doi/10.1080/10095020.2025.2459152
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