A novel hybrid neural network based on phase space reconstruction technique for daily river flow prediction
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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.
KeywordsArtificial neural network Gene expression programming Phase space reconstruction Prediction River flow
The authors are grateful to editor and anonymous reviewers for their helpful and constructive comments which greatly improved the quality of this paper.
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Conflict of interest
The authors declare that they have no conflict of interest.
Human and animals participants
This article does not contain any studies with human participants or animals performed by any of the authors.
- Bayazıt M (1988) Hydrologic models. ITU, IstanbulGoogle Scholar
- Ferreira C (2001a) Gene expression programming in problem solving. In: 6th online world conference on soft computing in industrial applications (invited tutorial)Google Scholar
- Jayawardena AW, Fernando DAK (1995) Artificial neural networks in hydrometeorological modelling. In: Proceedings of the fourth international conference on the application of artificial intelligence to civil and structural engineering-developments in neural networks and evolutionary computing in civil and structural engineering, Cambridge, UKGoogle Scholar
- Takens F (1981) Detecting strange attractors in turbulence. In: Rand DA, Young LS (eds) Lectures Notes in Mathematics, vol 898. Springer, New York, pp 366–381Google Scholar
- Terzi O (2011) Monthly river flow forecasting by data mining process, knowledge-oriented applications in data mining. prof. kimito funatsu (Ed.), ISBN: 978-953-307-154-1, InTech, http://www.intechopen.com/books/knowledge-oriented-applications-in-data-mining/monthly-river-flowforecasting-by-data-mining-process
- Wang WG, Zou S, Luo Zh, Zhang W, Chen D, Kong J (2014) Prediction of the reference evapotranspiration using a chaotic approach. Hindawi Publishing Corporation. Sci World J Article ID 347625, 13 ppGoogle Scholar