Prediction of the chrome distribution in subarctic Noyabrsk using co-kriging, generalized regression neural network, multilayer perceptron, and hybrid technics

Abstract

Combination of geostatistical interpolation (kriging) and machine learning (artificial neural networks, ANN) methods leads to an increase in the accuracy of forecasting. The paper considers the application of residual kriging of an artificial neural network to predicting the spatial contamination of the surface soil layer with chromium (Cr). We reviewed and compared two neural networks: the generalized regression neural network (GRNN) and multilayer perceptron (MLP), as well as the combined method: multilayer perceptron residual kriging (MLPRK). The study is based on the results of the screening of the surface soil layer in the subarctic Noyabrsk, Russia. The models are developed based on computer modeling with minimization of the RMSE. The MLPRK model showed the best prognostic accuracy.