Analysis and selection of models for reservoir inflow simulation in the Ba River Basin
Corresponding author:
Duy Dinh
Joint Vietnam-Russia Tropical Science and Technology Research Center
63 Nguyen Van Huyen Street, Nghia Do Ward, Ha Noi, Vietnam
Phone: 0989331023; Email: duydb.vrtc@gmail.com
ORCID iD: 0000-0003-3942-9165
Joint Vietnam-Russia Tropical Science and Technology Research Center
63 Nguyen Van Huyen Street, Nghia Do Ward, Ha Noi, Vietnam
Phone: 0989331023; Email: duydb.vrtc@gmail.com
ORCID iD: 0000-0003-3942-9165
Main Article Content
Abstract
Long-term and accurate daily and monthly streamflow data play a crucial role in understanding hydrological regimes and managing water resources; however, such records are often incomplete, particularly for reservoir inflows. This study addresses this gap by evaluating and comparing the performance of a process-based model (SWAT) and a data-driven model (Light Gradient Boosting Machine – LGBM) in simulating daily and monthly inflows to six major reservoirs in the Ba River Basin, Vietnam – a basin strongly influenced by reservoir regulation for irrigation and hydropower. Observed meteorological, hydrological, and satellite-derived datasets from 2016 to 2023 were utilized, with the period from 2017 to 2022 allocated for calibration and training, and 2016 and 2023 reserved for independent validation. SWAT calibration was performed automatically with SWAT-CUP, while the hyperparameters of LGBM were optimized using Bayesian Optimization (BOA). The results demonstrate the superior performance of LGBM in most cases, achieving higher Nash–Sutcliffe Efficiency (NSE) and Kling–Gupta Efficiency (KGE) scores, and reproducing key hydrological signatures more accurately than SWAT. Nevertheless, SWAT exhibited comparatively better performance in simulating monthly inflows to the An Khe reservoir, highlighting the potential advantages of process-based models under specific conditions.
Keywords
Hydrological modelling, SWAT, Machine learning, LGBM, Reservoir inflow
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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34. L. Bian, X. Qin, C. Zhang, P. Guo, and H. Wu, “Application, interpretability and prediction of machine learning method combined with LSTM and LightGBM—a case study for runoff simulation in an arid area” Journal of Hydrology, vol. 625, p. 130091, 2023. DOI: 10.1016/j.jhydrol.2023.130091
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