Modeling and Optimization of Stomatal Resistance in Olive under NaCl Salinity Stress by using Artificial Neural Network and Genetic Algorithm

Stomatal resistance has an important role in plant water exchange and photosynthesis under stress condition hence it is a key parameter in ecological and biological models. In the present research، optimum values of effective parameters on stomatal resistance of olive to achieve maximum stomatal resistance were determined. Salinity levels (0، 25، 50 and 100 mg/l NaCl)، percentage of leaf ions (chlorine، sodium، potassium، calcium، phosphor، magnesium)، K/Na ratio، wet weight of leaf (mg)، leaf area (cm2)، relative water content (%) and leaf area ratio (cm2/g) were considered as effective parameters in stomatal resistance of olive. The stomatal resistance was modeled as a function of the variables using a radial basis function (RBF) neural-network model. The predicted results demonstrated that the neural network could provide a good performance since prediction results were in rather good agreement with measured results. The maximum percentage error between predicted and experimental data was less than 2. 57% and the correlation coefficient between them was 0. 994. Also، the statistical comparison between the predicted and experimental data indicates the reliability of the predictions using a RBF neural-network model. The trained neural network is used as objective function to get optimal parameters using the genetic algorithm. The optimum values of the parameters obtained at a 100، 0. 15، 0. 57، 0. 78، 0. 32، 0. 06، 0. 17، 1. 29، 26. 63، 5. 03، 0. 76 and 72. 89 for the first parameter through the last parameter، respectively.