Abstract
Deep-learning neural network (DNN) models are currently gaining popularity in the field of hydrology as well as flow forecasting. This is because these models have proven that they are capable of producing short-period forecasts with high accuracy. However, studies on flow forecasting for hydrological stations located upstream of river basins are still uncommon as the limited number of input parameters can be gathered in such places. This study looked into the capacity of DNN models to predict discharge upstream of the Da River in Vietnam, where the topography is mostly mountainous. Streamflow data from the LaiChau hydrological station — the largest and most distant hydrological station upstream of the Da River — has been gathered and employed as input for three DNN models. These models are the LSTM (long short-term memory neural network), ANN (artificial neural network), and CNN (convolutional neural network). According to research findings, the performance of the LSTM and CNN models outperforms that of the ANN, which has an NSE coefficient of just approximately 0.91. LSTM has a slight advantage over CNN, although the difference is modest because their NSE coefficients are 0.97 and 0.96, respectively. This finding suggests that DNN models, particularly LSTM, can be a feasible alternative for upstream hydrological station discharge predictions.
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This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT)(No. 2020R1A2C1102758).
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All authors contributed to this study. Material preparation, data collection, and analysis were performed by Xuan-Hien Le, Duc Hai Nguyen, and Sungho Jung. The first draft of the manuscript was written by Xuan-Hien Le. The revised version was corrected by Giha Lee. All authors read and approved the final manuscript.
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Communicated by: H. Babaie
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Le, XH., Nguyen, D.H., Jung, S. et al. Deep neural network-based discharge prediction for upstream hydrological stations: a comparative study. Earth Sci Inform 16, 3113–3124 (2023). https://doi.org/10.1007/s12145-023-01082-9
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DOI: https://doi.org/10.1007/s12145-023-01082-9