Probability Forecast of Monthly Reservoir Inflow

Abstract

运用奇异谱分析(SSA)方法对资料系列进行降噪处理，采用人工神经网络(ANN)和支持向量机(SVM)建立确定性预报模型，构建基于Copula函数的联合分布，推求概率预报区间，并获得中值预报结果。以丹江口水库的月径流系列资料为研究对象，分析比较概率预报与确定性预报的结果。概率预报不仅可提高精度，同时还给出了预报区间，有利于水库调度决策人员定量考虑预报的不确定性，为决策支持提供技术支撑。 The Singular Spectrum Analysis (SSA) was applied to preprocess the original flow series, and Artificial Neural Network (ANN) and Support Vector Machine (SVM) were used to simulate and predict the reconstructed data series. The multivariate joint distribution based on copula function and probability forecast model were proposed. With a case study of Danjiangkou reservoir monthly inflow series, the probability forecast results were compared with that of deterministic model. It is shown that the probability forecast model not only can improve the accuracy of middle value prediction to a certain extent but also give probability interval, which helps reservoir managers to consider uncertainty quantitatively and provides technical support for decision-making.