Efficiency Comparison of Bayesian and MLP Neural Networks in Predicting Runoff to the Taleghan Dam

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.

Nafariyeh Zahra; SARAI TABRIZI MAHDI; BABAZADEH Hossein; KARDAN MOGHADDAM HAMID

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

Journal Paper

Paper Information

Journal: Environment and Water Engineering Year:2022 | Volume:8 | Issue:2 Page(s): 530-537

Download Full-Text

Persian Verion

View:

Download:

Cites:

Information Journal Paper

Title

Efficiency Comparison of Bayesian and MLP Neural Networks in Predicting Runoff to the Taleghan Dam

Author(s)

Nafariyeh Zahra | SARAI TABRIZI MAHDI | BABAZADEH Hossein | KARDAN MOGHADDAM HAMID | Issue Writer Certificate

Keywords

Runoff Prediction

Taleghan Dam

Bayesian Network

Perceptron Neural Network

Abstract

The importance of regulating the supply and demand regime shows the need for planning in the exploitation of surface water resources. The aim of this study was to compare the performance of two models of Bayesian Network (BN) with a probabilistic approach and MLP neural network for flow prediction and selection of the best structural model. Monthly meteorological data including rainfall, monthly average temperature, evaporation, and the volume of water transferred from five hydrometric stations were introduced as input data to the models, and runoff to the dam was considered as predictable. Input data with different layouts were introduced to BN and MLP models. The results were obtained by comparing 17 selected models according to the index criteria: Nash-Sutcliffe coefficient (NS), mean square error (MSE), mean square error root (RMSE), and MEAN absolute prediction error (MAPE). The best model in BN model with 43. 3% similarity and index criteria was estimated to be-3. 98, 300, 17. 3, and 0. 06, respectively. The MLP model with 80% similarity and index criteria were introduced as-10. 3,-8266, 23. 9, and 122. 3 in the best model, respectively. As a result, both models performed well in runoff estimation, but the BN model had much better accuracy in forecasting. Finally, a structural pattern with acceptable results in both MLP and BN models was identified.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans