3aa23dbf1902ae21f9d-3a7a 20240727014056081 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 07 30 2024 13 8 Hanoi University of Science and Technology, No. 1 Dai Co Viet, Hai Ba Trung, 100000, Hanoi, Vietnam. Bao Bui Quoc https://orcid.org/0000-0002-1158-9696 Hung Nguyen Khanh Hanoi University of Science and Technology, No. 1 Dai Co Viet, Hai Ba Trung, 100000, Hanoi, Vietnam. Hieu Nguyen Dac Department of Artificial Intelligence, Thuyloi University, 175 Tay Son, Dong Da, 100000, Hanoi, Vietnam. Dat Tran Anh Department of Artificial Intelligence, Thuyloi University, 175 Tay Son, Dong Da, 100000, Hanoi, Vietnam. Quang Chieu Ta https://orcid.org/0009-0001-5079-7762 Department of Artificial Intelligence, Thuyloi University, 175 Tay Son, Dong Da, 100000, Hanoi, Vietnam. Accurate and timely water level prediction is of paramount importance in various applications, including flood forecasting, hydroelectric power management, and environmental monitoring. Traditional recurrent neural network (RNN)-based methods have been widely used for this task. However, recent advancements in long-term time-series forecasting have introduced transformer-based models that have significantly improved the performance in time-series prediction tasks. In this research, we investigate the application of transformer-based models to the task of water level prediction, specifically focusing on the Nhat Le River Basin. We conducted multiple experiments with different test cases and various model architectures, providing specific analyses of the model’s prediction capabilities. The transformer-based models consistently outperformed conventional RNN-based methods across a range of evaluation metrics, including root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Moreover, these models exhibited excellent flood peak prediction accuracy, with errors consistently below 0.02 meters. The robustness and scalability of transformer-based models make them promising for accurate water-level predictions in real-world applications. 07 30 2024 21 28 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true International Journal of Innovative Technology and Exploring Engineering (IJITEE) This work was funded by SNDTWU, Mumbai. No conflicts of interest to the best of our knowledge. No, the article does not require ethical approval and consent to participate with evidence Not relevant. All authors have equal participation in this article. 10.35940/ijitee.H9936.13080724 https://www.ijitee.org/portfolio-item/H993613080724/