Chromium Distribution Forecasting Using Multilayer Perceptron Neural Network and Multilayer Perceptron Residual Kriging

It is known that combination of geostatistical interpolation techniques (e.g. kriging) and machine learning (e.g. neural networks) leads to better prediction accuracy and productivity. The paper deals with application of the artificial neural network residual kriging (ANNRK) to the spatial prediction of soil pollution by Chromium (Cr). In the work, we examined and compared two neural networks: Multilayer Perceptron (MLP) and Multilayer Perceptron Residual Kriging (MLPRK). The case study is based on the survey on surface contamination by Cr at the subarctic Noyabrsk, Russia. The proposed models have been built, implemented and validated using ArcGIS and MATLAB software. The models frameworks have been developed using a computer simulation based on a minimization of the root mean squared error (RMSE). Both models showed almost identical results.