Volume 14, Issue 27 (8-2023)
J Watershed Manage Res 2023, 14(27): 115-122 |
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Choubin M, Bashirgonbad M. (2023). Evaluation of IHACRES, Conceptual Rainfall Runoff Model and Artificial Neural Network Models in Simulation and Stream flow Prediction in Bakhtiary River Basin. J Watershed Manage Res. 14(27), 115-122. doi:10.61186/jwmr.14.27.115
URL: http://jwmr.sanru.ac.ir/article-1-1209-en.html
URL: http://jwmr.sanru.ac.ir/article-1-1209-en.html
1- Malayer University
Abstract: (1440 Views)
Extended 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.
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.
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