New Strategy for Optimizing Water Application under Trickle Irrigation
Publication: Journal of Irrigation and Drainage Engineering
Volume 128, Issue 5
Abstract
The determination of water application parameters for creating an optimal soil moisture profile represents a complex nonlinear optimization problem which renders traditional optimization into a cumbersome procedure. For this reason, an alternative methodology is proposed which combines a numerical subsurface flow model and artificial neural networks (ANN) for solving the problem in two, fully separate steps. The first step employs the flow model for calculating a large number of wetting profiles (output), obtained from a systematic variation of both water application and initial soil moisture (input). The resulting matrix of corresponding input/output values is used for training the ANN. The second step, the application of the fully trained ANN, then provides the irrigation parameters which range from a specified initial soil moisture to a desired crop-specific soil moisture profile. In order to avoid substantial disadvantages associated with the common feedforward backpropagation approach, a self-organizing topological feature map is implemented to perform this task. After a comprehensive sensitivity analysis, the new methodology is applied to the outcome of an irrigation experiment. The convincing results recommend the new methodology as a positive contribution towards an improved irrigation efficiency.
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Information
Published In
Journal of Irrigation and Drainage Engineering
Volume 128 • Issue 5 • October 2002
Pages: 287 - 297
Copyright
Copyright © 2002 American Society of Civil Engineers.
History
Received: May 31, 2001
Accepted: Sep 12, 2001
Published online: Sep 13, 2002
Published in print: Oct 2002
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