Generating Prediction Map for Geostatistical Data Based on an Adaptive Neural Network Using only Nearest Neighbors

This paper proposes a new interpolation method for spatial data based on an adaptive neural networks using only the different of x-coordinate, y-coordinate between observed data and their nearest neighbors, and values of neighbors surrounding unobserved location for training network architecture. Unobserved data are interpolated by function of its absolute location and relative location in x-coordinate and y-coordinate and corresponding value at absolute location of k-nearest neighbors. We compared our new proposed method by using observed data to generate prediction map using simulation data set and real world data set. The experimental results show that, by using relationship between nearest neighbors of unobserved point can achieve the good accuracy compare to competitive method for various data set and at different rate of missing.