By Topic

Modeling water infiltration rate under conventional tillage systems on a clay soil using artificial neural networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Abdulwahed Aboukarima ; Community College, Huraimla, King Saud University, P.O Box 300, 11962, Saudi Arabia ; Khaled Ahmed ; Abdulrahman Al-Janobi

This study presents the application of artificial neural networks for modeling the parameters of Lewis-Kostiakov infiltration under conventional tillage systems on a clay soil. The conventional tillage systems were moldboard, chisel and rotary plows. Water infiltration rate was defined experimentally by double ring infiltrometer. Artificial neural network estimation indicated strong correlations (R2 = 0.999) between the parameters of Lewis-Kostiakov infiltration (I=ktn) and affected variables (soil total porosity, soil moisture content, working index and aspect ratio). The simulated data from the developed artificial neural network formulated the parameters of Lewis-Kostiakov infiltration (k and n) as a function of tillage implement weight and width, speed and depth of plowing, tractor nominal power, soil total porosity and soil moisture content with R2 around 0.60. The developed model can help managers of irrigation systems to modify field practices during growing season to conserve irrigation water. The working index has more contribution on constant (k). Meanwhile, soil total porosity has more contribution on constant (n). Using the developed model, infiltration rate could be optimized during seedbed preparation process.

Published in:

World Automation Congress (WAC), 2010

Date of Conference:

19-23 Sept. 2010