Predicting river daily flow using wavelet-artificial neural networks based on regression analyses in comparison with artificial neural networks and support vector machine models | Neural Computing and Applications
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Predicting river daily flow using wavelet-artificial neural networks based on regression analyses in comparison with artificial neural networks and support vector machine models

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Abstract

This study investigates the ability of wavelet-artificial neural networks (WANN) for the prediction of short-term daily river flow. The WANN model is improved by conjunction of two methods, discrete wavelet transform and artificial neural networks (ANN) based on regression analyses, respectively. The proposed WANN models are applied to the daily flow data of Vanyar station, on the Ajichai River in the northwest region of Iran, and compared with the ANN and support vector machine (SVM) techniques. Mean square error (MSE), mean absolute error (MAE) and correlation coefficient (R) statistics are used for evaluating precision of the WANN, ANN and SVM models. Comparison results demonstrate that the WANN model performs better than the ANN and SVM models in short-term (1-, 2- and 3-day ahead) daily river flow prediction.

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Abbreviations

x(t):

Signal

t :

Integer time stages

j :

Integers that control the scale

k :

Integers that control the time

W j,k :

Wavelet coefficient

s:

Scale parameter

τ :

Time parameter

C p :

Mallows’ coefficient

i :

ith iteration

η :

Learning rate

θ :

Momentum value

E :

The sum of squared errors between observed and predicted data

ξ k :

Slack variables

ε :

Insensitivity loss function

C :

Positive trade-off factor

K :

Kernel function

n :

Number of support vectors

σ :

SVM model parameter

X mean :

Overall mean

S x :

Standard deviation

C sx :

Skewness

X min :

Minimum value

X max :

Maximum value

Q t :

Observed flow

Q min :

Minimum flow

Q max :

Maximum flow

Z i :

Normalized flow

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Authors and Affiliations

  1. Water Engineering Department, Tabriz University, Tabriz, Iran

    Maryam Shafaei

  2. Civil Engineering Department, Faculty of Architecture and Engineering, Canik Basari University, Samsun, Turkey

    Ozgur Kisi

Authors
  1. Maryam Shafaei
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  2. Ozgur Kisi
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Correspondence to Maryam Shafaei.

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Shafaei, M., Kisi, O. Predicting river daily flow using wavelet-artificial neural networks based on regression analyses in comparison with artificial neural networks and support vector machine models. Neural Comput & Applic 28 (Suppl 1), 15–28 (2017). https://doi.org/10.1007/s00521-016-2293-9

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  • DOI: https://doi.org/10.1007/s00521-016-2293-9

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