基于改进多元模糊均生函数的天然径流预测及其验证

Forecasting of natural flows using improved multiple fuzzy mean generating function model and its verification

天然径流的计算是区域水资源评价和水利水电工程设计的基础。在综合前人已有成果的基础上,提出了改进多元模糊均生函数时间序列预测模型。模型有机整合了模糊均生函数、逆推思想、提取优势周期、外部预测因子以及最优子集回归等思想和方法,解决了传统模型中周期算法的临近数据失效、模型结果仅具有统计意义等问题。模型被运用于渭河流域咸阳站的天然径流预测,并从分布特征、统计参数、降水径流关系三个方面对其结果的可靠性和方法的有效性进行了验证。结果表明,该模型适用于天然径流预测,且可获得较高的预测精度。

Calculation of natural or unimpaired flows is a key task in regional water resources assessment and hydroelectric project design. This paper presents an improved multiple fuzzy mean generating function model to forecast natural flows, based on several methods developed in previous studies. The model integrates this special function with the backstepping approach, extracting predominant periods and external factors, and optimal subset regression, and hence it is able to solve successfully the two frequently encountered problems in the conventional approaches： that they cannot effectively use the last data in a series, and that they produce results only statistically significant but inaccurate. We have verified this new model by testing its application to the natural runoff series at the Xianyang gauge station on the Wei River and examined the characteristics of probability distribution, statistical parameters, and precipitation-runoff relationship. Results show that it is an effective and rather accurate model.