An Application of Combined Geostatistics with Optimized Artificial Neural Network by Genetic Algorithm to estimate the distribution of Coccinella septempunctata (Col:. Coccinellidae) in the alfalfa farm of Bajgah

Today, with the advance statistical techniques and neural networks, predictive models of distribution were rapidly developed in ecology. Due to the difficulty of sampling, there are usually not enough samples in such studies. Therefore, in order to predict and mapping the distribution of Coccinella
septempunctata used the combination of the Kriging method with multilevel perceptron neural networks (MLP) combined with genetic algorithm at the farm level. Population data of pest was obtained in 2014 by sampling in 221 fixed points in the alfalfa farm of Bajgah. The data was interpolated by ordinary Kriging method with spherical variogram, which had the best performance, and introduced as a neural network input. To evaluate the ability combined geostatistics with optimized artificial neural network by genetic to predict the distribution used statistical comparison parameters such as mean, variance, statistical distribution and between predicted values and actual values. Results indicating that there was non-significant difference between statistical parameters such as average, variance, statistical distribution and also coefficient of determination in the observed and the estimated Coccinella septempunctata density. Our map showed that pest distribution was patchy.