Volume 22, Issue 2 (Summer 2018)                   jwss 2018, 22(2): 373-382 | Back to browse issues page

XML Persian Abstract Print

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

Heidari Z, Farasati M, Ghobadian R. Applicability of Support Vector Machine in Simulating Wetting Pattern under Trickle Irrigation. jwss 2018; 22 (2) :373-382
URL: http://jstnar.iut.ac.ir/article-1-3240-en.html
1. Department of Water Engineering, College of Agriculture, Razi University, Kermanshah, Iran. , 2. Department of Range and Watershed Management, College of Agricultural, Gonbad Kavous University, Gonbad, Iran. , farasati2760@gmail.com
Abstract:   (7155 Views)
To design cost-effective and efficient drip irrigation systems, it is necessary to know the vertical and horizontal advance of the wetting front under the point source; also, the proper management of drip irrigation systems requires an awareness of the soil water distribution. Many factors influence wetting pattern dimensions, including discharge, land slope, irrigation time and soil texture. The purpose of this study was to investigate the applicability of the support vector machine in simulating the wetting pattern under trickle irrigation. After preparing a physical model made of Plexiglas with specific dimensions and filled with silty clay loam soils, experiments were conducted in the irrigation laboratory of Razi University, Iran, with emitters of 2, 4, 6 and 8 l/hour discharge during the irrigation intervals of 2 hours and 24 hours redistribution and 0,5,15 and 20% slope with three replications. In this study, the statistical indicators R2, RMSE, MBE and MEF were used. R2 values for the wet depth, width and area were 0.96, 0.96 and 0.92, respectively. Regarding the MBE value, the SVM model estimated the wet width and depth parameters to be 3% less than the actual value, and simulated the wet area 2.04% less than the real value. Also, according to the MEF and RMSE values, the SVM model simulated the wet area parameter with more error.  Overall, the results showed that the SVM model had a high ability to estimate the wetting pattern parameters.
Full-Text [PDF 1935 kb]   (1455 Downloads)    
Type of Study: Research | Subject: Ggeneral
Received: 2016/03/5 | Accepted: 2017/10/22 | Published: 2018/09/15

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

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