中长期水文预报的最优组合模型研究
Mid and Long-Term Hydrological Forecasting Using Optimal Combined Model

作者: 刘 玥 , 刘 攀 , 李立平 :武汉大学水资源与水电工程科学国家重点实验室,武汉; 黄焕坤 , 虞云飞 , 张安标 :广东省飞来峡水利枢纽管理处,清远;

关键词: 飞来峡水库最优组合预报模型入库径流水文预报Feilaixia Reservoir Optimal Combined Forecasting Model Inflow Hydrological Forecasting

摘要: 本文建立了径流中长期预报的自回归、季节性自回归、门限自回归、最近邻抽样回归、人工神经网络、支持向量机等六种模型,并对这些模型结果进行综合,开展最优组合预报。以飞来峡水库为研究实例,选取平均绝对误差和均方误差作为评价指标,发现人工神经网络模型模拟精度较高;支持向量机模型模拟精度高、且具有最好的预报性能;最优组合预报模型综合各单一预报模型的优点,结果稳健、通用性强。

Abstract: This paper applies six models, including the autoregressive model, the seasonal autoregressive model, the threshold autoregressive model, the nearest neighbor bootstrap regressive model, the artificial neural network model and the support vector machine model into the mid and long-term hydrological forecasting. Based on Feilaixia reservoir project, the results show that the artificial neural network model is able to time series very well. The support vector machine model has the powerful ability of not only the simulation but also the forecasting. The results of those models were combined by the optimal combined forecasting model. The mean absolute error and the mean square error are selected as the measurements. Relying on the merits of each single model, the results of the optimal combined forecasting model work very well and are very well in robustness.

文章引用: 刘 玥 , 刘 攀 , 黄焕坤 , 李立平 , 虞云飞 , 张安标 (2013) 中长期水文预报的最优组合模型研究。 水资源研究, 2, 377-381. doi: 10.12677/JWRR.2013.26053

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