Prediction of wind drift and evaporation losses of a sprinkler irrigation system using principal component analysis and artificial neural network technique

  • Samy A Marey 1. Sciences, Technology and Innovation Unit, Rector’s for Graduate Studies & Scientific Research, King Saud University, PO Box 2454, Riyadh 11451, Saudi Arabia; 2. Agricultural Engineering Research Institute (AEnRI), Agricultural Research Centre, Egypt
  • Mohamed SA El Marazky 1. Department of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, PO Box 2460, Riyadh 11451, Saudi Arabia; 2. Agricultural Engineering Research Institute (AEnRI), Agricultural Research Centre, Egypt
  • Abdulwahed M Aboukarima 1. Department of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, PO Box 2460, Riyadh 11451, Saudi Arabia; 2. Agricultural Engineering Research Institute (AEnRI), Agricultural Research Centre, Egypt
Keywords: sprinkler irrigation systems, neural networks, modelling, evaporation and drift losses

Abstract

Principal component analysis was merged with the artificial neural network (ANN) technique to predict wind drift and evaporation losses (WDEL) from a sprinkler irrigation system. For this purpose, field experiments were conducted to determine WDEL under different conditions. Data from field experiments and previous studies were used as sample data to train the ANN model. Three models were developed to predict WDEL. In the first model (ANN1), 9 neurons (riser height, main nozzle diameter, auxiliary nozzle diameter, discharge rate of the main nozzle, discharge rate of the auxiliary nozzle, operating pressure, wind speed, air temperature and relative humidity) were used as the input layer. In the second model (ANN2), 7 neurons (riser height, operating pressure, wind speed, air temperature and relative humidity, diameter ratio and discharge ratio) were used as the input layer. The third model (ANN3) used a multivariate technique (PC1, PC2, and PC3). Results revealed that the ANN3 model had the highest coefficient of determination (R2 = 0.8349). The R2 values for the ANN1 and ANN2 models were 0.7792 and 0.4807, respectively. It can be concluded that the ANN3 model has the highest predictive capacity.

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Published
2018-07-31
Section
Research paper