RAINFALL RUNOFF MODELING USING GROUP METHOD OF DATA HANDLING (GMDH) AND ARTIFICIAL NEURAL NETWORK (ANN) IN IN POLROOD BASIN

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.

POURNEMAT ROUDSARI ADEL; QADERI KOUROSH; KARIMI GOOGHARI SHAHRAM

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

Journal Paper

Paper Information

Journal: JOURNAL OF WATERSHED MANAGEMENT RESEARCH Year:2015 | Volume:5 | Issue:10 Page(s): 68-84

Download Full-Text

Persian Verion

View:

1,257

Download:

Cites:

Information Journal Paper

Title

RAINFALL RUNOFF MODELING USING GROUP METHOD OF DATA HANDLING (GMDH) AND ARTIFICIAL NEURAL NETWORK (ANN) IN IN POLROOD BASIN

Author(s)

POURNEMAT ROUDSARI ADEL | QADERI KOUROSH | KARIMI GOOGHARI SHAHRAM | Issue Writer Certificate

Keywords

RAINFALL-RUNOFF MODELINGQ2

DATA DRIVEN METHODQ2

GMDHQ2

ANNQ2

POLROOD BASINQ1

Abstract

RAINFALL-RUNOFF MODELING is an essential process and very complicated phenomena that is necessary for proper reservoir system operation and successful water resources planning and management. There are different methods like conceptual and numerical methods for modeling of this process. Theoretically, a system modeling required explicit mathematical relationships between inputs and outputs variables. Developing such explicit model is very difficult because of unknown relationship between variables and substantial uncertainty of variables. In this case, DATA DRIVEN METHODs which are based on imprecise conditions and evolutionary algorithms have shown capabilities in many nonlinear systems identification and control issues. One of these intelligent methods is Group Method of Data Handling (GMDH). This method produce complex model during the performance evaluation of input and output information sets increasingly. So in this research we have developed a model based on GMDH for RAINFALL-RUNOFF MODELING in POLROOD BASIN located in North of Iran in Guilan province. Results have evaluated using statistical criteria and compared with an artificial neural network (ANN) model. Results have shown the high performance of two methods for rainfall runoff modeling in dairy scales. Based on statistical criteria, experiment results indicates that the GMDH model was powerful tool to model rainfall runoff time series and can be applied successfully in complex hydrological modeling.

Cites

No record.

References

No record.

Cite

APA: Copy

POURNEMAT ROUDSARI, ADEL, QADERI, KOUROSH, & KARIMI GOOGHARI, SHAHRAM. (2015). RAINFALL RUNOFF MODELING USING GROUP METHOD OF DATA HANDLING (GMDH) AND ARTIFICIAL NEURAL NETWORK (ANN) IN IN POLROOD BASIN. JOURNAL OF WATERSHED MANAGEMENT RESEARCH, 5(10), 68-84. SID. https://sid.ir/paper/230351/en

Vancouver: Copy

POURNEMAT ROUDSARI ADEL, QADERI KOUROSH, KARIMI GOOGHARI SHAHRAM. RAINFALL RUNOFF MODELING USING GROUP METHOD OF DATA HANDLING (GMDH) AND ARTIFICIAL NEURAL NETWORK (ANN) IN IN POLROOD BASIN. JOURNAL OF WATERSHED MANAGEMENT RESEARCH[Internet]. 2015;5(10):68-84. Available from: https://sid.ir/paper/230351/en

IEEE: Copy

ADEL POURNEMAT ROUDSARI, KOUROSH QADERI, and SHAHRAM KARIMI GOOGHARI, “RAINFALL RUNOFF MODELING USING GROUP METHOD OF DATA HANDLING (GMDH) AND ARTIFICIAL NEURAL NETWORK (ANN) IN IN POLROOD BASIN,” JOURNAL OF WATERSHED MANAGEMENT RESEARCH, vol. 5, no. 10, pp. 68–84, 2015, [Online]. Available: https://sid.ir/paper/230351/en

Related Journal Papers