The Ability of Artificial Neural Networks in Learning Dependency of Spatial Data‎

‎In conventional methods of spatial data analysis‎, ‎such as Kriging‎, ‎the dependency structure of data is estimated‎, ‎modeled‎, ‎and then used for data prediction‎. ‎In contrast‎, ‎the Artificial Neural Network (ANN) approach‎, ‎which is a data-driven approach‎, ‎does not model the data dependency structure‎. ‎Therefore‎, ‎an important question may arise here‎: ‎Does ANN use‎, ‎indirectly‎, ‎spatial dependency structure in data prediction? In this paper‎, ‎we want to answer this question through a simulation study‎. ‎Different dependent and independent spatial data sets are simulated under two spatial structures‎, ‎and the prediction accuracy of ANNs is compared for simulated data‎. ‎It is shown that neural network error for predicting dependent spatial data is much less than that of independent spatial data‎. ‎We conclude that the network can indirectly learn spatial dependence between the observations‎. ‎We also applied the ANN method to an experimentally obtained data set and compared its prediction accuracy with Kriging as a common geostatistical method‎. ‎The results showed that the neural network can be used as an alternative method for spatial data prediction.‎