Hydrological prediction under climate change requires representative data selection and adaptable model architecture. This study proposes a two-part methodology to improve deep learning performance in hydrological prediction. The first component, the representative hydrograph extraction technique (RHET), identifies representative inflow patterns from historical records using dynamic time warping (DTW) and K-medoids clustering. Inflow data are segmented by year, annual DTW distances are calculated, and central events are selected. Representative hydrographs serve as training input. The second component is the auto-setting artificial neural network (AS-ANN). The AS-ANN automatically determines its structural parameters by performing pre-training to evaluate performance across different configurations. The proposed approach was applied to the Daecheong Dam basin in South Korea and compared against an artificial neural network (ANN). Results show that the proposed model reduced the minimum root mean squared error (Min RMSE) by approximately 267.51 m3/day in the validation results and by approximately 53.04 m3/day in the prediction results compared to the ANN. Furthermore, the proposed model reduced the root mean square error by 57.28% and improved peak inflow prediction accuracy by 54.00%. The proposed RHET-based AS-ANN is expected to show good performance in learning and predicting hydrological data, including the data used in this study, by replacing existing ANNs.
Ryu et al. (Thu,) studied this question.