Use of analytical data and intelligent models in runoff precipitation simulation (Case study: Bazoft basin)

Document Type : Research Paper

Authors

1 Department of Hydrology and Water Resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

2 Professor, Department of Hydrology and Water Resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

3 Assistance Professor, Department of Hydrology and Water Resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

Abstract

Today, the use of intelligent models in simulating runoff has been widely used in water resources management. In this study, in order to predict the daily flow time series of the Morghak hydrometric station in Karun basin, an intelligent model of artificial neural network combined with wavelet analysis has been used. For this purpose, the ERA-INTRIM observational and analytical precipitation time series for 16 years (1378-1382) was decomposed by wavelet transform into frequency subsets, then each subset separately as input data to the artificial neural network model was introduced. The results showed that the analytical data have a high ability to simulate runoff precipitation models and can be a good alternative to observation data of rainfall stations. Also, according to the results of the wavelet transform technique, it can be effective in improving the performance of the simple ANN model for the Bazoft basin by 38% on a daily scale and 72% on a monthly scale.

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History

  • Receive Date: 04 January 2021
  • Revise Date: 02 May 2021
  • Accept Date: 02 May 2021
  • Publish Date: 02 May 2021

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