Hybrid neural network with genetic algorithms for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumbers field of Ramhormoz, Iran - Alireza Shabaninejad; Bahram Tafaghodinia; Nooshin Zandi Sohani (Persian Journal of Acarology)
Today, with the advanced statistical techniques and neural networks, predictive models of distribution have been rapidly developed in Ecology. Purpose of this research is to predict and map the distribution of Tetranychus urticae Koch (Acari: Tetranychidae) using MLP neural networks combined with genetic algorithm in surface of farm. Population data of pest was obtained in 2016 by sampling in 100 fixed points in cucumber field in Ramhormoz city, Khuzestan province, Iran. To evaluate the ability of neural networks combined with genetic algorithm to predict the distribution, statistical comparison between the predicted and actual values of some parameters such as variance, statistical distribution and linear regression coefficient was performed. Results showed that in training and test phases of neural network combined genetic algorithm, there was no significant difference between variance and statistical distribution of actual values and predicted values, but distribution was no significant. Our map showed that patchy pest distribution offers a large potential for using site-specific pest control on this field.