水资源研究  >> Vol. 3 No. 4 (August 2014)

长江干流站中长期径流预报方法研究
Research on Medium and Long Term Runoff Forecast in Yangtze River Basin

DOI: 10.12677/JWRR.2014.34035, PDF, HTML, 下载: 2,155  浏览: 7,142  国家自然科学基金支持

作者: 贾军伟, 张利平:武汉大学水资源与水电工程国家重点实验室,武汉;刘 恋, 闪丽洁:水资源安全保障湖北省协同创新中心,武汉

关键词: 逐步回归支持向量机长江流域中长期径流预报Stepwise Regression Support Vector Machine Yangtze River Basin Medium and Long-Term Runoff Forecast

摘要: 长江流域是我国经济最发达的地区之一,水资源量的多少直接影响着该地区经济社会的发展,因此准确的中长期预报对于水库群联合调度、水资源调配和合理利用具有重要的意义。本文以长江流域屏山、宜昌、大通和汉口4个站点为研究对象,依据74项大气环流指数和前期径流共75项预报因子,采用相关系数法初选及逐步回归法优选预报因子,建立了基于支持向量机的月、旬径流预报模型,定量分析了模型在长江流域的适用性,并与人工神经网络预测模型进行比较,结果表明该模型在长江流域中长期径流月、旬预报中,检验期平均合格率分别为49.29%和54.49%,达不到实际应用的需求,而旬尺度计算月径流的结果优于月尺度的模拟结果,有较好的预报精度,可为长江流域水文预报工作提供参考。相对而言,支持向量机模型的预测精度优于人工神经网络模型。
Abstract: Yangtze River is one of China’s most economically developed regions, and the quantity of water directly affects the economic and social development in the region, so an accurate medium and long-term forecast is significant for multi-reservoir scheduling, water resources allocation and ra-tional utilization. In this paper, Pingshan, Yichang, Datong and Hankou four stations in the Yangtze River basin were selected as the research objects, based on 75 predictors, including 74 atmospheric circulation index and pre-runoff, using the correlation coefficient method to preliminary select the predictors and stepwise regression method to optimize the predictors, a runoff forecasting model of monthly scale and ten-day scale based on the Support vector machine (SVM) was established. And the applicability of the model in the Yangtze River basin was quantitatively analyzed. In addition, the prediction precision of the model was compared with that of artificial neural network prediction model. The results indicate that the model based on SVM can’t meet the actual application requirements, because the average qualified rates during the test period were only respectively 49.29% and 54.49% in the medium and long-term runoff forecasting of monthly scale and ten-day scale. But the monthly runoff results calculated by ten-day scale are superior to those by monthly scale. The former can provide a reference for the work of the Yangtze River hydrological forecasting. Relatively speaking, the prediction precision of the model based on Support vector machine (SVM) is better than that of artificial neural network model.

文章引用: 贾军伟, 张利平, 刘恋, 闪丽洁. 长江干流站中长期径流预报方法研究[J]. 水资源研究, 2014, 3(4): 283-290. http://dx.doi.org/10.12677/JWRR.2014.34035

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