Machine learning algorithms in geostatistical data analysis: formulation and observation

Abstract

Geostatistical analyses on spatiotemporal datasets have been increasingly popular on diverse disciplines including environmental sciences, soil and earth studies, meteorology, hydrology, and oceanography. They help in documenting and summarizing information to understand the variation of the process and other parameters. Due to high spatial variability and noise in the dataset, traditional geostatistical tools often fail to produce desirable results. In view of this, the present study proposes two machine learning methods, namely artificial neural network (ANN) and k-nearest neighbour (kNN), along with the kriging interpolation to derive temporal distribution of soil parameters. Results in terms of smooth porosity maps demonstrate the effectiveness of the proposed algorithm.