Comparison of Machine Learning Methods and Ordinary Kriging for Gravimetric Mapping: Application to Yagoua Area (Northern Cameroon) - Mfenjou Martin Luther; Boroh Andre William; Kasi Njeudjang; Kabe Moukete Eric Bruno; Amaya Adama (Journal of Engineering)
This work focuses on the comparison of a number of machine learning methods (random forest, support vector machine (SVM), and artificial neural networks (ANN)) and ordinary kriging (OK). It is based on OK. Indeed, OK, which is a stochastic spatial interpolation method, predicts the value of a natural phenomenon at unsampled sites, and it is an unbiased linear combination with a minimal variance that yields observations on the model at neighbouring sites. So, knowing the various improvements made by machine learning–based methods, we used them. The analysis of the different methods provides a basis for comparison according to the defined indicators. A better gravimetric mapping requires the sampling of a certain number of points whose densities will make it possible to carry out geostatistical analyses and interpretations and thus be able to estimate the deposit. Thus, concerning the prediction of the parameters used in the detection of gravity anomalies, OK is better with R2=0.99. Regarding the prediction of gravity anomalies, OK is able to reproduce a good variability of the anomalies, but when the spatial variability interval of the ANNs is close, it is then better indicated than OK. However, an increase in the data size would allow us to see the best performance of machine learning–based methods in gravity mapping.