Research on Surface Water Quality Prediction based on a CNN-BiLSTM-Attention Combined Deep Learning Approach
E3S Web of Conf., 393 (2023) 01007
Published online: 2023-06-02
Articles citing this article
The Citing articles tool gives a list of articles citing the current article.
The citing articles come from EDP Sciences database, as well as other publishers participating in CrossRef Cited-by Linking Program. You can set up your personal account to receive an email alert each time this article is cited by a new article (see the menu on the right-hand side of the abstract page).
Cited article:
This article has been cited by the following article(s):
365 (2026)
https://doi.org/10.1007/978-3-032-10602-5_13
A hybrid deep learning model with topology-aware attention for multi-station water quality prediction at basin scale
Ziqi Yan, Lei Liu, Huaming Li and Feier WangJournal of Water Process Engineering 88 110174 (2026)
https://doi.org/10.1016/j.jwpe.2026.110174
Time-series inversion of heavy metal and non-heavy metal water quality parameters and reveal their driving mechanisms using CNN-BiLSTM-Attention and PLS-SEM
Bolin Fu, Xiao Ning, Xiaoming Si, Jianming Deng, Yuanyuan Gan, Donglin Fan and Zongjin LuEcological Indicators 189 115207 (2026)
https://doi.org/10.1016/j.ecolind.2026.115207
A Comparative Study of Advanced Transformer Learning Frameworks for Water Potability Analysis Using Physicochemical Parameters
Enes Algül, Saadin Oyucu, Onur Polat, Hüseyin Çelik, Süleyman Ekşi, Faruk Kurker and Ahmet AksozApplied Sciences 15 (13) 7262 (2025)
https://doi.org/10.3390/app15137262
Comparative insights into independent and hybrid modeling strategies for effective river water level prediction and management
Osama Ragab Ibrahim, Aynaz Vafaei, Sanam Ansari, Erfan Abdi, Maryam Sifaei and Seyed Mahdi Jafari MohammadiModeling Earth Systems and Environment 11 (6) (2025)
https://doi.org/10.1007/s40808-025-02611-9