Evaluation of IHACRES, Conceptual Rainfall Runoff Model and Artificial Neural Network Models in Simulation and Stream flow Prediction in Bakhtiary River Basin

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Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:
Introduction and Objective

In recent years, river flow forecasting is one of the most important issues for water resources management in Iran. This prediction requires statistics and information, unfortunately, most of the basins of the country lack data of the desired quantity and quality.

Material and Methods

Therefore, hydrological modelling and the use of artificial intelligence are examples of solutions that are used to solve this challenge in hydrology. The criteria for selecting the appropriate model for this process are to evaluate the performance of the models according to the hydrological conditions of each region. In this research, IHACRES model and Artificial Neural Network (ANN) were used to predict the streamflow in Bakhtiary basin. The data from 1984 to 1994 were used as calibration period and the data from 1995 to 2006 were used for validation.

Results

The evaluation results of the hydrological model and the artificial neural network were evaluated using Kling-Gupta, Nash-Sutcliffe indices, coefficient of determination, mean squared error and absolute mean error. Results showed that the artificial neural network had better results in the simulation in all the evaluated evaluation criteria.

Conclusion

According to the results of the methods used in the research, the artificial neural network method has a more accurate prediction of the Bakhtiary river flow than the hydrological model.

Language:
Persian
Published:
Journal of Watershed Management Research, Volume:14 Issue: 27, 2023
Pages:
115 to 122
https://www.magiran.com/p2623199  
سامانه نویسندگان
  • Author (2)
    Mohammad Bashirgonbad
    Assistant Professor Natural Resources and environment, Malayer University, Mlaair, Iran
    Bashirgonbad، Mohammad
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