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A new approach to flow simulation using hybrid models

Authors
  • Solgi, Abazar1
  • Zarei, Heidar1
  • Nourani, Vahid2
  • Bahmani, Ramin1
  • 1 Shahid Chamran University of Ahvaz, Department of Water Resources Engineering, Faculty of Water Sciences Engineering, Nahavand, Iran , Nahavand (Iran)
  • 2 University of Tabriz, Department of Water Resources Engineering, Faculty of Civil Engineering, Nahavand, Iran , Nahavand (Iran)
Type
Published Article
Journal
Applied Water Science
Publisher
Springer International Publishing
Publication Date
Jan 24, 2017
Volume
7
Issue
7
Pages
3691–3706
Identifiers
DOI: 10.1007/s13201-016-0515-z
Source
Springer Nature
Keywords
License
Green

Abstract

The necessity of flow prediction in rivers, for proper management of water resource, and the need for determining the inflow to the dam reservoir, designing efficient flood warning systems and so forth, have always led water researchers to think about models with high-speed response and low error. In the recent years, the development of Artificial Neural Networks and Wavelet theory and using the combination of models help researchers to estimate the river flow better and better. In this study, daily and monthly scales were used for simulating the flow of Gamasiyab River, Nahavand, Iran. The first simulation was done using two types of ANN and ANFIS models. Then, using wavelet theory and decomposing input signals of the used parameters, sub-signals were obtained and were fed into the ANN and ANFIS to obtain hybrid models of WANN and WANFIS. In this study, in addition to the parameters of precipitation and flow, parameters of temperature and evaporation were used to analyze their effects on the simulation. The results showed that using wavelet transform improved the performance of the models in both monthly and daily scale. However, it had a better effect on the monthly scale and the WANFIS was the best model.

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