Integrating Hydrological–Hydraulic–AI (LSTM) Models for Improved Water Level Forecasting: Red River - Thai Binh Basin Cover

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Integrating Hydrological–Hydraulic–AI (LSTM) Models for Improved Water Level Forecasting: Red River - Thai Binh Basin

Civil and Environmental Engineering

By: Tri Doan Quang, Nhat Nguyen Van and Tuyet Quach Thi Thanh

Open Access

|Nov 2025

Authors

Tri Doan Quang

doanquangtrikttv@gmail.com

Hydrometeorological Information and Data Center, Viet Nam Meteorological and Hydrological Administration, Hanoi, Vietnam

Nhat Nguyen Van

Hydrometeorological Information and Data Center, Viet Nam Meteorological and Hydrological Administration, Hanoi, Vietnam

Tuyet Quach Thi Thanh

Hydrometeorological Information and Data Center, Viet Nam Meteorological and Hydrological Administration, Hanoi, Vietnam

DOI: https://doi.org/10.2478/cee-2026-0042 | Journal eISSN: 2199-6512 | Journal ISSN: 1336-5835

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Language: English

Submitted on: Sep 8, 2025

Accepted on: Sep 24, 2025

Published on: Nov 12, 2025

Published by: University of Žilina

In partnership with: Paradigm Publishing Services

Publication frequency: 2 issues per year

Keywords:

Water level forecasting,

Red River - Thai Binh Basin,

NAM-MIKE 11-LSTM,

Flood risk management

Related subjects:

Business and economics,

Political economics,

Economic theory, systems and structures,

Business management,

Environmental management

© 2025 Tri Doan Quang, Nhat Nguyen Van, Tuyet Quach Thi Thanh, published by University of Žilina
This work is licensed under the Creative Commons Attribution 4.0 License.