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首页> 外文期刊>Journal of Hydroinformatics >Multiscale nonlinear model for monthly streamflow forecasting: a wavelet-based approach
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Multiscale nonlinear model for monthly streamflow forecasting: a wavelet-based approach

机译：用于月流量预测的多尺度非线性模型：基于小波的方法

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摘要

The dynamics of the streamflow in rivers involve nonlinear and multiscale phenomena. An attempt is made to develop nonlinear models combining wavelet decomposition with volterra models. This paper describes a methodology to develop one-month-ahead forecasts of streamflow using multiscale nonlinear models. The method uses the concept of multiresolution decomposition using wavelets in order to represent the underlying integrated streamflow dynamics and this information, across scales, is then linked together using the first- and second-order volterra kernels. The model is applied to 30 river data series from the western USA. The mean monthly data series of 30 rivers are grouped under the categories low, medium and high. The study indicated the presence of multiscale phenomena and discernable nonlinear characteristics in the streamflow data. Detailed analyses and results are presented only for three stations, selected to represent the low-flow, medium-flow and high-flow categories, respectively. The proposed model performance is good for all the flow regimes when compared with both the ARMA-type models as well as nonlinear models based on chaos theory.
机译：河流中的水流动力学涉及非线性和多尺度现象。尝试开发结合小波分解和Volterra模型的非线性模型。本文介绍了一种使用多尺度非线性模型开发流量提前一个月预测的方法。该方法使用使用小波的多分辨率分解的概念来表示基本的集成流动力学，然后使用一阶和二阶Volterra内核将跨尺度的此信息链接在一起。该模型已应用于美国西部的30个河流数据系列。 30条河流的月平均数据系列分为低，中和高类别。研究表明流数据中存在多尺度现象和可辨别的非线性特征。仅针对三个站分别显示了低流量，中流量和高流量类别的详细分析和结果。与ARMA型模型和基于混沌理论的非线性模型相比，所提出的模型性能对所有流动态都具有良好的性能。

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