Modeling Estimation of Suspended Sediment Rate in Pasikhan River Using Decision Tree Artificial Neural Network

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.

Mir Fallah Nasiri Seied saman; AMIRI EBRAHIM; Shadabi Bojand Mahboubeh

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

Journal Paper

Paper Information

Journal: JOURNAL OF WATER AND SOIL RESOURCES CONSERVATION Year:2021 | Volume:10 | Issue:2 Page(s): 31-41

Download Full-Text

Persian Verion

View:

242

Download:

Cites:

Information Journal Paper

Title

Modeling Estimation of Suspended Sediment Rate in Pasikhan River Using Decision Tree Artificial Neural Network

Author(s)

Mir Fallah Nasiri Seied saman | AMIRI EBRAHIM | Shadabi Bojand Mahboubeh | Issue Writer Certificate

Keywords

Decision treeQ1

Suspended sedimentQ2

Psikhan riverQ1

Artificial neural networkQ2

modelingQ1

Abstract

Accurate estimation of sediment transport in rivers due to erosion is an important factor for the management of hydrological and ecological projects. Artificial neural networks are of great importance for many reasons, such as the ability to detect patterns, the good relationship between input and output, and the need for less input data to predict Suspended sedimentation. Accordingly, the present study attempts to model the estimation of Suspended sediment content in the Pasikhan River using the Artificial neural network of the M5 Decision tree. The amount of sediment in rivers is subject to many parameters of river geometry, hydraulic flow and sediment properties. For this reason, in this study, it has been tried to reduce the number of effective parameters by first dimensioning the effective parameters on sediment transport capacity. The results showed that the initial Decision tree, the M5 tree, does not require pruning and is suitable for use. Three parameters of determination coefficient (R2), mean relative error (ME) and mean squared error (RMSE) were used to evaluate the accuracy of the prediction model. The obtained values for these three parameters were 0. 851, 1037. 64 and 941. 32, respectively, indicating the suitability of these three parameters. Comparison of Suspended sediment yield from Decision tree model with Pasikhan River measurement data showed that the coefficient of determination was 0. 8953 which is a very good value. The results showed that this model is effective in predicting Suspended sediment content in the Pasikhan River.

Cites

No record.

References

No record.

Cite

APA: Copy

Mir Fallah Nasiri, Seied saman, AMIRI, EBRAHIM, & Shadabi Bojand, Mahboubeh. (2021). Modeling Estimation of Suspended Sediment Rate in Pasikhan River Using Decision Tree Artificial Neural Network. JOURNAL OF WATER AND SOIL RESOURCES CONSERVATION, 10(2 ), 31-41. SID. https://sid.ir/paper/412933/en

Vancouver: Copy

Mir Fallah Nasiri Seied saman, AMIRI EBRAHIM, Shadabi Bojand Mahboubeh. Modeling Estimation of Suspended Sediment Rate in Pasikhan River Using Decision Tree Artificial Neural Network. JOURNAL OF WATER AND SOIL RESOURCES CONSERVATION[Internet]. 2021;10(2 ):31-41. Available from: https://sid.ir/paper/412933/en

IEEE: Copy

Seied saman Mir Fallah Nasiri, EBRAHIM AMIRI, and Mahboubeh Shadabi Bojand, “Modeling Estimation of Suspended Sediment Rate in Pasikhan River Using Decision Tree Artificial Neural Network,” JOURNAL OF WATER AND SOIL RESOURCES CONSERVATION, vol. 10, no. 2 , pp. 31–41, 2021, [Online]. Available: https://sid.ir/paper/412933/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops