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Artificial Neural Network and Support Vector Machine Models for Inflow Prediction of Dam Reservoir (Case Study: Zayandehroud Dam Reservoir)

  • Mohammad Babaei
  • Ramtin MoeiniEmail author
  • Eghbal Ehsanzadeh
Article
  • 41 Downloads

Abstract

Inflow prediction of reservoirs is of considerable importance due to its application in water resources management related to downstream water release planning and flood protection. Therefore, in this research, different new input patterns for predicting inflow to Zayandehroud dam reservoir is proposed employing artificial neural network (ANN) and support vector machine (SVM) models. Nine different models with different patterns of input data such as inflow to the dam reservoir considering time duration lags, time index, and monthly rainfall of Ghaleh-Shahrokh station have been proposed to predict the inflow to the dam reservoir. Comparison of the results indicates that the ninth proposed model has the least error for inflow prediction in which the results of SVM model outperform those of ANN model. That is, the least error has been obtained using the ninth SVM (ANN) model with correlation coefficient (R) values of 0.8962 (0.89296), 0.9303 (0.92983) and 0.9622 (0.95333) and root mean squared error (RMSE) values of 47.9346 (48.5441), 42.69093 (43.748) and 23.56193 (28.5125) for training, validation and test data, respectively.

Keywords

Inflow discharge prediction Artificial neural network Support vector machine Input data pattern Zayandehroud dam 

Notes

Compliance with Ethical Standards

Conflict of Interest

None.

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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Mohammad Babaei
    • 1
  • Ramtin Moeini
    • 1
    Email author
  • Eghbal Ehsanzadeh
    • 2
  1. 1.Department of Civil Engineering, Faculty of Civil Engineering and TransportationUniversity of IsfahanIsfahanIran
  2. 2.Department of Water Engineering, Faculty of AgricultureIlam UniversityIlamIran

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