A novel hybrid neural network based on phase space reconstruction technique for daily river flow prediction

Delafrouz Hadi; Ghaheri Abbas; Ghorbani Mohammad Ali

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A novel hybrid neural network based on phase space reconstruction technique for daily river flow prediction

机译：一种基于期间河流预测相空间重构技术的新型混合神经网络

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摘要

The main purpose of this study is to construct a new hybrid model (PSR-ANN) by combining phase space reconstruction (PSR) and artificial neural network (ANN) techniques to raise the accuracy for the prediction of daily river flow. For this purpose, river flow data at three measurement stations of the USA were used. To reconstruct the phase space and determine the input data for the PSR-ANN method, the delay time and embedding dimension were calculated by average mutual information and false nearest neighbors analysis. The presence of chaotic dynamics in the used data was identified by the correlation dimension methods. The results of the PSR-ANN, pure ANN and gene expression programming (GEP) models were inter-compared using the Nash-Sutcliffe and root-mean-square error criteria. The inter-comparisons showed that the proposed PSR-ANN method provides the best prediction of daily river flow. Moreover, the ANN model showed higher ability than the pure GEP in estimation of the river flow.

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来源
《Soft computing: A fusion of foundations, methodologies and applications》 |2018年第7期|共11页
作者
Delafrouz Hadi; Ghaheri Abbas; Ghorbani Mohammad Ali;
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作者单位

Iran Univ Sci &

Technol Sch Civil Engn Tehran Iran;

Iran Univ Sci &

Technol Sch Civil Engn Tehran Iran;

Univ Tabriz Dept Water Engn Tabriz Iran;

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原文格式 PDF
正文语种 eng
中图分类计算机软件;
关键词
Artificial neural network; Gene expression programming; Phase space reconstruction; Prediction; River flow;

机译：人工神经网络;基因表达编程;相空间重建;预测;河流;

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机译：一种基于期间河流预测相空间重构技术的新型混合神经网络
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机译：基于混沌预处理和相空间重构的脑电神经网络分类。

A novel hybrid neural network based on phase space reconstruction technique for daily river flow prediction

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