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...

Full description

Saved in:
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
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.tandfonline.com/doi/10.1080/10095020.2025.2459152

Similar Items