Volume 25, Issue 2 (Summer 2021)                   jwss 2021, 25(2): 77-90 | Back to browse issues page


XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Roshun S H, Shahedi K, Habibnejad Roshan M, Chormanski J. Comparison of the Performance of ANN and SVM Methods in Rainfall-Runoff Process Modeling (Case Study: North Karun Watershed). jwss 2021; 25 (2) :77-90
URL: http://jstnar.iut.ac.ir/article-1-4016-en.html
1. Watershed Management Engineering, Sari Agricultural Sciences and Natural Resources University. Mazandaran, Iran. , h.roshun2011@gmail.com
Abstract:   (3944 Views)
The simulation of the rainfall-runoff process in the watershed has particular importance for a better understanding of hydrologic issues, water resources management, river engineering, flood control structures, and flood storage. In this study, to simulate the rainfall-runoff process, rainfall and discharge data were used in the period 1997-2017. After data qualitative control, rainfall, and discharge delays were determined using the coefficients of autocorrelation, partial autocorrelation, and cross-correlation in R Studio software. Then, the effective parameters and the optimum combination were determined by the Gamma test method and used to implement the model under three different scenarios in MATLAB software. Gamma test results showed that today's precipitation parameters, precipitation of the previous day, discharge of the previous day, and discharge of two days ago have the greatest effect on the outflow of the basin. Also, the Pt Qt-1 and Pt Pt-1 Qt-1 Qt-2 Qt-3 combinations were selected as the most suitable input combinations for modeling. The results of the modeling showed that in the support vector machine model, the Radial Base kernel Function (RBF) has a better performance than multiple and linear kernels. Also, the performance of the Artificial Neural Network model (ANN) is better than the Support Vector Machine model (SVM) with Radial Base kernel Function (RBF).
Full-Text [PDF 865 kb]   (910 Downloads)    
Type of Study: Research | Subject: Ggeneral
Received: 2020/04/22 | Accepted: 2020/09/28 | Published: 2021/09/1

Add your comments about this article : Your username or Email:
CAPTCHA

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2024 CC BY-NC 4.0 | JWSS - Isfahan University of Technology

Designed & Developed by : Yektaweb