非平稳时间序列的组合模型及其在中长期水文预报中的应用
The Combinatory model of Non-Stationary Time Series and its Application to Medium-Long Terms River Flow Forecasting
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摘要: 基于水文序列是一类周期性非平稳时间序列的特点,本文提出了一种适合描述这一特点 的组合模型.这个组合模型的优点在于它能较充分地利用历史数据中蕴含的信息,即它既能 利用以年为采样周期的时间序列的相关信息,又能利用以月为采样周期的时间序列的相关信 息;编制了FORTRAN语言程序,将它应用于河流的中长期水文预报.从文中所列出的汉口和 龙滩水文站的向前1至12个月的水文预报结果可以看出,组合模型的预报效果是较好的.Abstract: Based on the nature that river flow series is a kind of seasonal non-stationary time series, a proper combinatory model describing this nature is developed in this paper. The advantage of the combinatory model is that information in historical data can be fully utilized, i.e., the correlated information of time series sampled both yearly or monthly can all be used. FORTRAN programs have been written to forecast medium-long terms flow of river by means of this combinatory model. The monthly mean flow forecasted one to twelve months ahead of time at Hankou Station on the Long River and Longtan Station on the Hongshuhe River show that the forecasting effect is satisfactory. The work is part of a water resource research work mandated by the Ministry of Hydro- power.
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