水库月径流概率预报研究
Probability Forecast of Monthly Reservoir Inflow
DOI: 10.12677/JWRR.2017.61001, PDF, HTML, XML, 下载: 1,576  浏览: 2,391  国家自然科学基金支持
作者: 何绍坤, 郭生练, 刘章君, 尹家波, 吴旭树：武汉大学水资源与水电工程科学国家重点实验室，湖北 武汉；水资源安全保障湖北省协同创新中心，湖北 武汉
关键词: 奇异谱分析；人工神经网络；支持向量机；Copula函数；条件分布；Singular Spectrum Analysis； Artificial Neural Networks； SVM； Copula； Conditional Distribution
摘要: 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。
Abstract: 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.
文章引用：何绍坤, 郭生练, 刘章君, 尹家波, 吴旭树. 水库月径流概率预报研究[J]. 水资源研究, 2017, 6(1): 1-8. https://doi.org/10.12677/JWRR.2017.61001

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