Comparison of Kriging and artificial neural network models for the prediction of spatial data

Abstract

The prediction of a spatial variable is of particular importance when analyzing spatial data. The main objective of this study is to evaluate and compare the performance of several prediction-based methods in spatial prediction through a simulation study. The studied methods include ordinary Kriging (OK), along with several neural network methods including Multi-Layer Perceptron network (MLP), Ensemble Neural Networks (ENN), and Radial Basis Function (RBF) network. We simulated several spatial datasets with three different scenarios due to changes in data stationarity and isotropy. The performance of methods was evaluated using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Concordance Correlation Coefficient (CCC) indexes. Although the results of the simulation study revealed that the performance of the neural network in spatial prediction is weaker than the Kriging method, but it can still be a good competitor for Kriging.