Comparison of Artificial Neural Network, Random Forest and Random Perceptron Forest for Forecasting the Spatial Impurity Distribution

摘要

The paper is present a comparison of modern approaches for predicting the spatial distribution in the upper soil layer of a chemical element chromium (Cr), which had spots of anomalously high concentration in the investigated region. The distribution of a normally distributed element copper (Cu) was also predicted. The data were obtained as a result of soil screening in the city of Tarko-Sale, Russia. Models based on artificial neural networks (multilayer perceptron MLP), random forests (RF), and also a model based on a random forest in which MLP used as a tree - a random perceptron forest (RMLPF) - were considered. The models were implemented in MATLAB. Approaches using artificial neural networks (MLP and RMLPF) were significantly more accurate for anomalously distributed Cr. Models based on RF algorithms proved to be more accurate for normally distributed copper. In general, the proposed model RMLPF was the most universal and accurate.