Multi-step-ahead reservoir inflow forecasts using neural networks with ensemble method

• Main Authors: Ying-Chin Lo, 羅英秦
• Other Authors: Fi-John Chang
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/93972619180271311873

碩士 === 國立臺灣大學 === 生物環境系統工程學研究所 === 101 === Due to unique geographical location of Taiwan, an average of 3.5 typhoons attack Taiwan each year. In addition, the particular topographical terrains of Taiwan make rivers short and steep such that rivers rapidly flow from catchments to reservoirs within a few hours during typhoon events. It will be very helpful and useful for reservoir operation management if reservoir inflow information can be provided in the next few hours after typhoons initially arrive. This study investigates the rainfall-runoff process of reservoir catchment by using two different rainfall information: rain gauge precipitation data and radar rainfall data (QPESUMS: Quantitative Precipitation Estimation and Segregation Using Multiple Sensors). The Zhengwen Reservoir catchment is the study area. The correlation analysis results show that it takes only two hours for rainfall to travel from catchment to reservoir in the study area. Therefore, this study aims to build up multi-step-ahead reservoir inflow forecast models through artificial neural networks based on QPESUMS and inflow information. The results indicate that all the BPNN, ANFIS and RNN models have excellent estimation performance. The BPNN model performs the best for one- to three-hour-ahead forecasts, while the RNN model has the best performance for four- to six-hour-ahead forecasts. The ANFIS model is superior to the other models for peak flow forecasts. The results demonstrate that each neural network has its own distinct advantages from others. Ensemble forecasting was originated from Atmospheric sciences and has been developed for years. In this study, we build up an ensemble forecast model by incorporating the outputs of three constructed forecast models into the BPNN to produce multi-step-ahead reservoir inflow forecasts, and further conduct the sensitivity analysis to summarize the weights of individual models incorporated in the ensemble forecast model for each time step. The results demonstrate that the ensemble forecast model can provide more reliable and accurate multi-step-ahead reservoir inflow forecasts than individual models incorporated.