Comparison of optimized artificial neural network performance with imperialist competitive algorithm and shuffled frog leaping algorithm to predict the distribution pattern of Coccinella septempunctata (Col: Coccinellidae ) in alfalfa fields of zarghan

Nowadays, many researchers have paid attention to explaining the patterns of insect dispersion using interpolation and density estimation methods in order to investigate the possibility of proper integrated management with the location of pests. This study was conducted to predict and map the spatial distribution of the Coccinella septempunctata using multilayer perceptron neural networks (MLPs) combined with the imperialist competitive algorithm and shuffled frog leaping algorithm at the field level. Data have been obtained through100 samples taking from the surface of a hay field of zarghan area in 2019. To evaluate the neural networks used and compare their performance, statistical parameters such as statistical distribution, mean comparison and coefficient of explanation between the spatially predicted values by the neural network and their actual values were used to predict the distribution of this species. The results showed that in the training and experimental phases, there was no significant difference between the values of statistical distribution and the mean of real and predicted spatial data sets of this species combined by neural network with shuffled frog leaping algorithm. Shuffled frog leaping algorithm was more accurate in detecting the distribution. Our map showed that pest distribution was patchy