Hybrid MLP-IDW approach based on nearest neighbor for spatial prediction

Conventional methods of spatial prediction, such as Kriging, require assumptions such as stationarity and isotropy, which are not easy to evaluate, and often do not hold for spatial data. For these methods, the spatial dependency structure between data should be accurately modeled, which requires expert knowledge in spatial statistics. On the other hand, spatial prediction using artificial neural network (ANN) has attracted considerable interest due to ANN’s ability in learning from data without the need for complex and specialized assumptions. However, ANN models require suitable input variables for better and efficient spatial prediction. This paper aims to improve the accuracy of ANNs spatial prediction using neighboring information. Given the general principle that ”closer spatial data are more dependent”, we tried to somehow enter data dependency into the network by using the neighboring observations. We proposed a hybrid model of ANN and inverse distance weighting, based on nearby observations. We also proposed an ANN-based model for spatial prediction based on weighted values of nearby observations. The accuracy of the models was compared through a simulation study. The results showed that using neighboring information to train ANN, dramatically increases the prediction accuracy.