Simulating of Changes in Water Distribution Uniformity Coefficient in Classic Stationary Sprinkler Irrigation Using Data-Mining Models

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Ahmadzadeh Kaleybar Fariborz; Shahmohammadi Kalalagh Shahram; Fard Moradinia Sina

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Journal Paper

Paper Information

Journal: Journal of New Approaches in Water Engineering and Environment Year:2024 | Volume:3 | Issue:2 Page(s): 118-135

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Title

Simulating of Changes in Water Distribution Uniformity Coefficient in Classic Stationary Sprinkler Irrigation Using Data-Mining Models

Author(s)

Ahmadzadeh Kaleybar Fariborz | Shahmohammadi Kalalagh Shahram | Fard Moradinia Sina | Issue Writer Certificate

Keywords

Gene expression programming

Malekan plain

Sensitivity analysis

Support Vector Machine

Performance Evaluation

Abstract

The coefficient of water distribution uniformity in sprinkler irrigation systems is one of the important indicators that are effective in evaluating their performance and only high values can justify the implementation of these systems. The purpose of this research is to use Support Vector Machine (SVM) and Gene expression programming (GEP) models to simulate the coefficient of water distribution uniformity in the farm-conditions of Malekan plain in the northwest of Iran, which is in the catchment area of the Urmia lake is experiencing severe water stress.Field tests were carried out on seven farms equipped with a classic stationary sprinkler irrigation system with a movable sprinkler (Komet 162, 163) with variables of sprinkler intervals on laterals and manifolds, operating pressure and wind speed, and distribution uniformity coefficient data were obtained. The values of the indicators (RMSE, MAE, R2) were obtained in the training and test steps, respectively (3.5087, 2.6827, 0.8634) and (1.1787, 0.9494, 0.9833) for GEP. The values of the evaluation indices (RMSE, MAE, R2) for the most optimal SVM model in the test and training steps were obtained (4.8917, 4.2704, 0.7884) and (2.6790, 2.4113, 0.9185) respectively. In the training step, the value of CU(DDR(max)) for GEP and SVM model was calculated as 7.0540 and 5.2925 respectively. The value of this index in the test step for these two models was 20.83 and 9.28 respectively. The comparison of the value of this index also showed that the GEP model is more accurate than the SVM model.

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