"Technical Report" Performance Comparison of IHACRES Model and Artificial Neural Network to Predict the Flow of Sivand 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.

Author(s)

Karimpour Fahimeh | DARZI NAFTCHALI ABDULLAH | NADI MEHDI | Issue Writer Certificate

Keywords

WatershedQ1

FloodQ1

Static networksQ1

Dynamic networksQ1

Abstract

The accurate determination of river flow in Watersheds without sufficient data is one of the major challenges in hydrology. In this regard, given the diversity of existing hydrological models, selection of an appropriate model requires evaluation of the performance of the hydrological models in each region. The objective of this study was to compare the performance of artificial neural network (ANN) and IHACRES integrated model to predict the flow of sivand river in Tashak Bakhtegan Watershed located in Fars province as a warm and arid area. Calibration and validation procedures were done by using data from 1982-1995 and 1996-2012, respectively. Neural Network Toolbox of MATLAB software were used to evaluate the capabilities of neural networks. In both calibration and validation periods, simulated flows by the IHACRES model for Flood flows, were less than the observed data. The determination coefficients of the model during calibration and validation were 0. 62 and 0. 54, respectively. The determination coefficients of dynamic neural networks and static neural networks during calibration and validation ranged from 0. 88-0. 94 and 0. 51-0. 69, respectively. The results demonstrated that artificial neural networks predicted monthly flow of sivand river more accurately than the IHACRES model.

Cites

No record.

References

No record.

Cite

APA: Copy

Karimpour, Fahimeh, DARZI NAFTCHALI, ABDULLAH, & NADI, MEHDI. (2020). "Technical Report" Performance Comparison of IHACRES Model and Artificial Neural Network to Predict the Flow of Sivand River. JOURNAL OF WATERSHED MANAGEMENT RESEARCH, 10(20 ), 262-267. SID. https://sid.ir/paper/407133/en

Vancouver: Copy

Karimpour Fahimeh, DARZI NAFTCHALI ABDULLAH, NADI MEHDI. "Technical Report" Performance Comparison of IHACRES Model and Artificial Neural Network to Predict the Flow of Sivand River. JOURNAL OF WATERSHED MANAGEMENT RESEARCH[Internet]. 2020;10(20 ):262-267. Available from: https://sid.ir/paper/407133/en

IEEE: Copy

Fahimeh Karimpour, ABDULLAH DARZI NAFTCHALI, and MEHDI NADI, “"Technical Report" Performance Comparison of IHACRES Model and Artificial Neural Network to Predict the Flow of Sivand River,” JOURNAL OF WATERSHED MANAGEMENT RESEARCH, vol. 10, no. 20 , pp. 262–267, 2020, [Online]. Available: https://sid.ir/paper/407133/en

Related Journal Papers