Assessing LSTM algorithm performance for daily runoff prediction at Hoa Duyet hydrological station, Vietnam
Email:
phuongtltv@utc.edu.vn
Tóm tắt
Accurate discharge forecasting is crucial for effective water resource management, flood risk mitigation, and hydrological planning, particularly in regions prone to extreme weather events. This study evaluates the performance of a Long Short-Term Memory (LSTM) network in predicting river discharge at the Hoa Duyet hydrology station. The prediction model is developed using rainfall data from the Ngan Sau river basin, collected over a 49-year period from 1975 to 2023. The model's accuracy was assessed across a range of lead times (1-day, 3-day, 5-day, and 7-day) and time lag length (365, 90, 30, 10, and 7 days). It was revealed that short-term forecasts (e.g., 1-day) consistently achieved high accuracy, with the time lag length 90-day yielding the best Nash-Sutcliffe Efficiency (NSE) of 0.864. Seasonal analysis indicated the reliability of the model for the rainy season (NSE = 0.863), but lower accuracy during the dry season (NSE = 0.582), reflecting the challenges of predicting low-flow dynamics. The model also demonstrated reasonable accuracy in predicting annual runoff peaks, with an average error of 91.75 m³/s, although discrepancies were observed in specific years. These findings highlight the LSTM model's capacity to adapt to diverse temporal configurations and hydrological conditions, making it a valuable tool for discharge prediction while emphasizing the need for further optimization in low-flow and extreme event scenariosTài liệu tham khảo
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[2]. F. Kratzert, D. Klotz, C. Brenner, K. Schulz, M. Herrnegger, Rainfall-runoff modelling using Long Short-Term Memory (LSTM) networks, Hydrol. Earth Syst. Sci., 22 (2018) 6005-6022. https://doi.org/10.5194/hess-22-6005-2018
[3]. N. V. Tuan, D. Q. Trung, N. V. Duc, Hydrologic forecasting, Vietnam national university press, Hanoi, Vietnam, 2003 (in Vietnamese).
[4]. V. V. Nghi, Numerical hydrological models, Science and Technics publishing house, Vietnam, 2015 (in Vietnamese).
[5]. H. N. Binh, Artificial neural network with an error backpropagation network algorithm and application, Journal of Water resources and Environmental Engineering, 7 (2024) 105-109 (in Vietnamese).
[6]. H. N. Binh, L. T. V. Ha, Application of ebn to predict the dry discharge in Ta Trach river, Thua Thien Hue province, Vietnam, Transport and Communications Science Journal, 71 (2020) 1000-1015 (in Vietnamese). https://doi.org/10.47869/tcsj.71.8.10
[7]. Cheng, M., Fang, F., Kinouchi, T., Navon, I.M., Pain, C.C., Long lead-time daily and monthly streamflow forecasting using machine learning methods, Journal of Hydrology 590 (2020) 125376. https://doi.org/10.1016/j.jhydrol.2020.125376
[8]. N. D. Hanh, N. T. Giang, L. X. Hoa, T. N. Vinh, H. D. Nguyen, Multi-step-ahead prediction of water levels using machine learning: A comparative analysis in the Vietnamese Mekong Delta, Vietnam Journal of Earth Sciences, 46 (2024), 468-488. https://doi.org/10.15625/2615-9783/21067
[9]. H. Apaydin, M. T. Sattari, K. Falsafian, R. Prasad, Artificial intelligence modelling integrated with Singular Spectral analysis and Seasonal-Trend decomposition using Loess approaches for streamflow predictions, Journal of Hydrology, 600 (2021) 126506. https://doi.org/10.1016/j.jhydrol.2021.126506
[10]. B. Lindemann, T. Müller, H. Vietz, N. Jazdi, M. Weyrich, A survey on long short term memory networks for time series prediction, Procedia CIRP, 99 (2021) 650–655. https://doi.org/10.1016/j.procir.2021.03.088
[11]. S. Hochreiter, J. Schmidhuber, Long Short-Term Memory, Neural Comput., 9 (1997) 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
[12]. T. N. Vinh, D. D. Duc, P. H. D. Binh, D. D. Kha, T. N. Anh, N. N. Ha, N. T. Giang, Data-Driven Dam Outflow Prediction Using Deep Learning with Simultaneous Selection of Input Predictors and Hyperparameters Using the Bayesian Optimization Algorithm, Water Recourses Management, 38 (2024) 401–421. https://doi.org/10.1007/s11269-023-03677-9
[13]. V. Moreido, B. Gartsman, D. P. Solomatine, Z. Suchilina, How Well Can Machine Learning Models Perform without Hydrologists? Application of Rational Feature Selection to Improve Hydrological Forecasting, Water, 13 (2021) 1696-1710. https://doi.org/10.3390/w13121696
[14]. G. Papacharalampous, H. Tyralis, A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting, Fronter in Water, 4 (2022) 1-21. https://doi.org/10.3389/frwa.2022.961954
[15]. F. Kratzert, D. Klotz, C. Brenner, K. Schulz, M. Herrnegger, Rainfall - runoff modelling using Long Short-Term Memory (LSTM) networks, Hydrology and Earth System Sciences, 22 (2018) 6005-6022. https://doi.org/10.5194/hess-22-6005-2018
[16]. C. P. Van, H. Le, Estimation of the daily flow in river basins using the data-driven model and traditional approaches: an application in the Hieu river basin, Vietnam, Water Pract. Technol., 18 (2023) 215–230. https://doi.org/10.2166/wpt.2022.166
Tải xuống
Chưa có dữ liệu thống kê
Nhận bài
30/12/2024
Nhận bài sửa
28/02/2025
Chấp nhận đăng
07/03/2025
Xuất bản
15/05/2025
Chuyên mục
Công trình khoa học
Kiểu trích dẫn
Hoang Nam, B., Tran Thu, P., Hoang Duc, V., & Le Van, N. (1747242000). Assessing LSTM algorithm performance for daily runoff prediction at Hoa Duyet hydrological station, Vietnam. Tạp Chí Khoa Học Giao Thông Vận Tải, 76(4), 556-567. https://doi.org/10.47869/tcsj.76.4.9
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