Predicting distribution pattern of Bemisia tabaci G. ( (Hem.: Aleyrodidae) by Hybrid neural network With Particle Swarm Optimization Algorithm

Today, with the Advance statistical techniques and neural networks, predictive models of distribution was rapidly developed in Ecology. Purpose of this study was predict and Mapping distribution of Bemisia tabaci G. using MLP neural networks combined with Particle Swarm Optimization in surface of cucumber field. Population data of pest was obtained in 2017 by sampling in 100 fixed points in a fallow field in Ramhormoz, to evaluate the ability of neural networks combined with Particle Swarm Optimization to predict the distribution used statistical comparison parameters such as mean, variance, statistical distribution and coefficient determination of linear regression among predicted values and actual values. Results showed that in training and test phases of neural network combined Particle Swarm Optimization algorithm, was no significant effect between variance, mean and statistical distribution of actual values and predicted values. Our map showed that patchy pest distribution offers large potential for using site-specific pest control on this field.