PREDICTION OF MONTHLY STREAMFLOW USING DATA-DRIVEN MODELS (TECHNICAL NOTE)

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.

AHANI A.; SHOURIAN M.

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

Journal Paper

Paper Information

Journal: IRAN-WATER RESOURCES RESEARCH Year:2017 | Volume:13 | Issue:2 Page(s): 207-214

Download Full-Text

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

Persian Verion

View:

680

Download:

Cites:

Information Journal Paper

Title

PREDICTION OF MONTHLY STREAMFLOW USING DATA-DRIVEN MODELS (TECHNICAL NOTE)

Author(s)

AHANI A. | SHOURIAN M. | Issue Writer Certificate

Keywords

STREAMFLOW FORECASTINGQ3

DATA-DRIVEN MODELINGQ1

MLRQ1

KNNQ1

ANNQ2

ANFISQ1

Abstract

In recent years, DATA-DRIVEN MODELING techniques have gained numerous applications in hydrology and water resources studies. River runoff estimation and forecasting is one of the research fields in which these techniques have several applications. In the current study, four DATA-DRIVEN MODELING techniques of multiple linear regression, K-nearest neighbors, artificial neural networks, and adaptive neuro-fuzzy inference systems have been used to form runoff forecasting models and then their results have been evaluated. Also, effects of using some different scenarios to select predictor variables have been studied. It was evident from the results that using flow data related to one or two months ago in the predictor variables dataset can improve the accuracy of the results. In addition, comparison of general performances of the modeling techniques showed superiority of KNN models results among the studied models. The selected KNN model presented best performance with a linear correlation coefficient equal to 0.84 between observed flow data and predicted values and a RMSE equal to 2.64.

Cites

No record.

References

No record.

Cite

APA: Copy

AHANI, A., & SHOURIAN, M.. (2017). PREDICTION OF MONTHLY STREAMFLOW USING DATA-DRIVEN MODELS (TECHNICAL NOTE). IRAN-WATER RESOURCES RESEARCH, 13(2 ), 207-214. SID. https://sid.ir/paper/99839/en

Vancouver: Copy

AHANI A., SHOURIAN M.. PREDICTION OF MONTHLY STREAMFLOW USING DATA-DRIVEN MODELS (TECHNICAL NOTE). IRAN-WATER RESOURCES RESEARCH[Internet]. 2017;13(2 ):207-214. Available from: https://sid.ir/paper/99839/en

IEEE: Copy

A. AHANI, and M. SHOURIAN, “PREDICTION OF MONTHLY STREAMFLOW USING DATA-DRIVEN MODELS (TECHNICAL NOTE),” IRAN-WATER RESOURCES RESEARCH, vol. 13, no. 2 , pp. 207–214, 2017, [Online]. Available: https://sid.ir/paper/99839/en

Related Journal Papers