Daily river flow estimation based on intelligent models, case study: Mahabad River

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Abbasi Abbas; Khalili Keivan; Behmanesh Javad; Shirzad Akbar

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: WATERSHED ENGINEERING AND MANAGEMENT Year:2021 | Volume:13 | Issue:3 Page(s): 614-624

Download Full-Text

Persian Verion

View:

Download:

Cites:

Information Journal Paper

Title

Daily river flow estimation based on intelligent models, case study: Mahabad River

Author(s)

Abbasi Abbas | Khalili Keivan | Behmanesh Javad | Shirzad Akbar | Issue Writer Certificate

Keywords

Bayesian Network

flood

Gene Expression Programming

prediction

Urmia Lake

Abstract

The correct and accurate estimation of river flow can play an important role in reducing the effects of flood damage. In this research, Gene Expression Programming (GEP) model and Bayesian Network (BN) were used to predict daily flow of Mahabad River in Urmia Lake Basin. Accordingly, four input models with a delay of one to four days used to estimate daily flow at time t+1 over a 23-years period and 75% of data was used to train the models and 25% of the remaining data was used for the test stage. Results showed that the best model in both methods was the input pattern with three-time lags. Also, based on the correlation coefficient (R), Root Mean Square Error (RMSE) and Nash-Sutcliffe (E) coefficient in the test stage of the GEP method with R=0.902, RMSE=2.71(m3s-1) and E=0.812 compared to the BN method with R=0.905, RMSE=2.679(m3s-1( and E=0.817 is more accurate. In general, both methods have acceptable accuracy and are they relatively similar, but because of the simpler modeling, Bayesian Network method can be used as an efficient method for predicting river flow.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans