An Ensemble Technique to Daily Rainfall Forecasting Based on SSA

Jifu Nong
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引用次数: 1

Abstract

In this paper, we have proposed a constructive methodology for temporal data learning supported by results and prescriptions related to the embedding theorem, and using the singular spectrum analysis both in order to reduce the effects of the possible discontinuity of the signal and to implement an efficient ensemble method. In this paper we present new results concerning the application of this approach to the forecasting of the individual rainfall intensities series collected by 135 stations. The average RMS error of the obtained forecasting is less than 3 mm of rain.
基于SSA的日降水预报集成技术
在本文中,我们提出了一种建设性的时间数据学习方法,该方法由与嵌入定理相关的结果和处方支持,并使用奇异谱分析来减少信号可能的不连续的影响,并实现有效的集成方法。在本文中,我们提出了关于将这种方法应用于135个站点收集的单个降雨强度系列预报的新结果。所得预报的平均均方根误差小于3毫米。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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