Search

Forecasting soil pollution is an important part of environmental protection around the world. The work is devoted to the use of artificial neural networks for spatial prediction of the anomalously distributed chemical element Chromium (Cr). Generalized regression neural networks (GRNN) and multilayer perceptrons (MLP) are classes of neural networks widely used for continuous mapping of functions. Each network has its pros and cons; however, both demonstrated quick preparation and good modeling capabilities. In the paper, we studied and compared two neural networks: GRNN and MLP. The present study is based on real data sets on surface contamination of Cr at a specific location in subarctic Novy Urengoy, Russia, obtained during previous screening. The network structures were chosen during computer modeling, based on the minimization of the root mean squared error (RMSE). GRNN (anisotropy) showed the best predictive accuracy in comparison with MLP and kriging. [ABSTRACT FROM AUTHOR]

In this work we suggest the way for selection of the type and structure of an artificial neural network (ANN) for restoring the topsoil spatial distribution of chemical elements. To restore the surface distribution using computer modeling, various types and structures of ANN were chosen. For each chemical element, its own ANN was selected and its own estimation of the prediction accuracy was used. Comparison of the concentrations distributions of chemical elements were made by different ANNs with preliminary known values of concentrations. It is known that trained ANNs provide high prediction accuracy. The proposed approach allows choosing type and structure of a neural network for an arbitrary site, which is one of the main difficulties in modeling the distribution of chemical elements by the ANN method. [ABSTRACT FROM AUTHOR]

The study is based on the data obtained as a result of soil screening in the city of Noyabrsk, Russia. A comparison of two types of neural networks most commonly used in this type of research was carried out: multi-layer perceptron (MLP), generalized regression neural network (GRNN), and a combined MLP and ordinary kriging approach (MLPRK) for predicting the spatial distribution of the chemical element Chromium (Cr) in the surface layer of the urbanized territory. The model structures were developed using computer modeling, based on minimizing of a root mean squared error (RMSE). As input parameters, the spatial coordinates were used, and the concentration of Cr - as the output. The hybrid MLPRK approach showed the best prognostic accuracy. [ABSTRACT FROM AUTHOR]