摘要
中长期径流具有非线性时变特点,带有高度的复杂性和不确定性,使用单一算法或模型的预报结果往往不令人满意。因此,本文首先利用GFS降雨预报信息并采用多元线性回归、BP神经网络、季节自回归和新安江模型对浑江桓仁水库流域进行旬径流预报;然后使用自适应联邦滤波算法对四个模型的预报信息进行融合、校正;最后应用结果表明,多模型信息融合能够增强预报的稳定性并提高预报精度,为中长期径流预报方法提供了一定的参考。
This paper presents a concept of fusion information collected from different rainfall-runoff models to produce medium and long-term hydrological forecasts. The objective of data fusion is to enhance forecasting stability and accuracy. We describe a framework of adaptive federated filter algorithm based on the Kalman filtering algorithm, and four models for fusion data collection, i.e. multiple linear regression (MLR) , BP neural network (BP), seasonal autoregressive (SAR) and Xinanjiang model (XAJ). The fusion model was validated by simulation and the results suggest that it is a useful model for medium and long-term hydrological forecasting.
出处
《水力发电学报》
EI
CSCD
北大核心
2013年第6期11-18,共8页
Journal of Hydroelectric Engineering
基金
国家自然科学基金资助项目(51379027
51109025)
教育部博士点基金(20100041120004)
中央高校基本科研业务费专项(DUT13JS06)
水利部公益性行业专项(201001024)
关键词
水文学
中长期
水文预报
多模型
联邦滤波
信息融合
hydrology
medium and long-term
hydrological forecasting
multi-model
federated filter
information fusion