Search

Forecasting the soil pollution is a considerable field of study in the light of the concern of environmental protection issues worldwide. The work deals with the application of artificial neural networks combining with residual kriging (ANNRK) to the spatial prediction of the abnormally distributed chemical element Chromium (Cr). It is known that combination of geostatistical interpolation approaches (kriging) and neural networks leads to better prediction accuracy and productivity. In the work, we compared two combined techniques: generalized regression neural network residual kriging (GRNNRK) and multi-layer perceptron residual kriging (MLPRK). The case study is based on the real data sets on surface contamination by Cr at a particular location of the subarctic Novy Urengoy, Russia, obtained during the previously conducted screening. The networks structures have been chosen during a computer simulation based on a minimization of the root mean squared error (RMSE). MLPRK showed the best predictive accuracy comparing to kriging and GRNNRK. [ABSTRACT FROM AUTHOR]

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]

The paper deals with application of Multilayer Perceptron (MLP) to the prediction of Chromium (Cr) distribution features in different soil. The distributions of the element content in urban soils are intersectional and multifactorial in nature. The local features of soil formation are necessary taking into account to forecast the chemical elements content at the unmonitored sites. The raw data of elements content in the topsoil are the results of chemical analysis of the soil sample, selected in three cities of the Yamalo-Nenets Autonomous Okrug - Noyabrsk, Tarko-Sale and Novy Urengoy. During the soil surveys, 486 soil specimens were taken from the residual zones. According to spatial distribution of total chromium concentration of the spots with high-level concentration were recognized in Tarko-Sale and Novy Urengoy. Mean total concentration of chromium in specimens of the spot subgroups is by an order of magnitude higher than in subgroups of urban background. The MLP network structure is selected during the computer simulation based on the minimization of the RMSE individually for each area. The better accuracy is for Noyabrsk and for Tarko-Sale. MLP is suitable for modeling both the normal and abnormal spatial distribution of pollutants. For the distribution without marked features, like Novy Urengoy area, MLP model detects enough high error. [ABSTRACT FROM AUTHOR]

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. [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]