Journal of Hydrology | 2021
A weights combined model for middle and long-term streamflow forecasts and its value to hydropower maximization
Abstract
Abstract Stochastic nature of streamflow poses significant challenges in attaining a reliable forecasting model. In general, variational mode decomposition (VMD) can improve the forecast performance but easily expose the sub-signals to boundary effect. Furthermore, one model is not able to adapt all properties of different sub-series. Accordingly, we have two aims in this study. One is to propose an adaptive weight combined forecasting model to improve the middle and long-term streamflow forecast skill. It adapts the boundary effect in such a way that its inputs come from decomposition during calibration sets, and outputs are extracted from decomposition during the entire series. The other one is to link system performance improvement, i.e., the forecast skill, to the forecast value to address the gap in methodologies appropriate for data-limited locations (only hydrological time series collected). Four artificial intelligence-based models coupled with adaptive appendant and parameter optimization are developed as hybrid adaptive (HA) for forecasting each sub-signal decomposed by VMD in the proposed forecast model. The multi-objective grey wolf optimization (MOGWO) algorithm is then employed to combine individual forecasts based on performance-based weighting strategies and provide the Pareto-optimal ensemble forecasts. The proposed model is applied to forecast streamflow 1 to 6\xa0months ahead of two stations in the Yellow River, China, and the results show that the ensemble forecasts can increase the values of Nash-Sutcliffe efficiency coefficient by 0.10\xa0~\xa07.96% and reducing the values of root mean squared error by 1.08\xa0~\xa032.11% compared to the HA model. The relationship between the forecast skill and its value can be strongly affected by decision-makers priorities, but the relative improvement in hydropower generation obtained by the compromised forecasts going from 0.02% to 3.39% indicates that improved forecasts are potentially valuable for informing strategic decisions.