Utilization of the Long Short-Term Memory network for predicting streamflow in ungauged basins in Korea

Hydrological prediction is essential for managing and preserving headwater, wetland, and rural basins, yet it is difficult due to a lack of data. The Long Short-Term Memory (LSTM) network is a promising deep learning approach and has demonstrated excellent performance in streamflow prediction. However, due to the characteristics that require abundant and high-quality observations, most LSTM applications focus on investigating performance for data-rich basins. To investigate the applicability of LSTM in ungauged basins without hydrological observations, we introduce and test an approach to predict streamflow in ungauged basins using the LSTM network that has learned the integration of data from multiple gauged basins. Four learning strategies are constructed using various datasets such as ground-observed meteorological data, satellite data, and hydro-geomorphological characteristics. As a result, the LSTM network that learned the meteorological data in multiple gauged basins satisfactorily predicted streamflow in ungauged basins. The LSTM network that learned the satellite data further showed affirmative results. On the other hand, the LSTM, which additionally learned the hydro-geomorphological characteristics of the basins, exposed the need for improvement, such as securing additional various types of training data. In addition, it was recognized that additional efforts were needed to solve overfitting and out-of-distribution prediction problems. However, this approach achieved model performance deriving metrics above threshold values. These results show the potential of LSTM for streamflow prediction in ungauged basins including wetlands. •LSTM, which was learned by integrating data in multiple gauged basins, shows the potential for PUB.•The streamflow predicted in ungauged basins by LSTM shows performance metrics above the thresholds.•However, more data in gauged basins are needed to solve the overfitting and out-of-distribution prediction problems.