COMPARISON OF REGRESSION, ARIMA AND ANN MODELS FOR RESERVOIR INFLOW FORECASTING USING SNOWMELT EQUIVALENT (A CASE STUDY OF KARAJ)

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.

MOHAMMADI K.; ESLAMI H.R.; DAYANI DARDASHTI SH.

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

Journal Paper

Paper Information

Journal: JOURNAL OF AGRICULTURAL SCIENCE AND TECHNOLOGY (JAST) Year:2005 | Volume:7 | Issue:1-2 Page(s): 17-30

Download Full-Text

View:

853

Download:

600

Cites:

Information Journal Paper

Title

COMPARISON OF REGRESSION, ARIMA AND ANN MODELS FOR RESERVOIR INFLOW FORECASTING USING SNOWMELT EQUIVALENT (A CASE STUDY OF KARAJ)

Author(s)

MOHAMMADI K. | ESLAMI H.R. | DAYANI DARDASHTI SH. | Issue Writer Certificate

Keywords

RIVER FLOW FORECASTING

REGRESSION ANALYSIS

ARTIFICIAL NEURAL NETWORK

ARIMA

Abstract

The present study aims at applying different methods for predicting spring inflow to the Amir Kabir reservoir in the Karaj river watershed, located to the northwest of Tehran (Iran). Three different methods, ARTIFICIAL NEURAL NETWORK (ANN), ARIMA time series and REGRESSION ANALYSIS between some hydroclimatological data and inflow, were used to predict the spring inflow. The spring inflow accounts for almost 60 percent of annual inflow to the reservoir. Twenty five years of observed data were used to train or calibrate the models and five years were applied for testing. The performances of models were compared and the ANN model was found to model the flows better. Thus, ANN can be an effective tool for reservoir inflow forecasting in the Amir Kabir reservoir using snowmelt equivalent data.

Cites

No record.

References

No record.

Cite

APA: Copy

MOHAMMADI, K., ESLAMI, H.R., & DAYANI DARDASHTI, SH.. (2005). COMPARISON OF REGRESSION, ARIMA AND ANN MODELS FOR RESERVOIR INFLOW FORECASTING USING SNOWMELT EQUIVALENT (A CASE STUDY OF KARAJ). JOURNAL OF AGRICULTURAL SCIENCE AND TECHNOLOGY (JAST), 7(1-2), 17-30. SID. https://sid.ir/paper/529253/en

Vancouver: Copy

MOHAMMADI K., ESLAMI H.R., DAYANI DARDASHTI SH.. COMPARISON OF REGRESSION, ARIMA AND ANN MODELS FOR RESERVOIR INFLOW FORECASTING USING SNOWMELT EQUIVALENT (A CASE STUDY OF KARAJ). JOURNAL OF AGRICULTURAL SCIENCE AND TECHNOLOGY (JAST)[Internet]. 2005;7(1-2):17-30. Available from: https://sid.ir/paper/529253/en

IEEE: Copy

K. MOHAMMADI, H.R. ESLAMI, and SH. DAYANI DARDASHTI, “COMPARISON OF REGRESSION, ARIMA AND ANN MODELS FOR RESERVOIR INFLOW FORECASTING USING SNOWMELT EQUIVALENT (A CASE STUDY OF KARAJ),” JOURNAL OF AGRICULTURAL SCIENCE AND TECHNOLOGY (JAST), vol. 7, no. 1-2, pp. 17–30, 2005, [Online]. Available: https://sid.ir/paper/529253/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops