SELF ORGANIZING NEURAL MAPS IN THE PROBLEMS OF ECOLOGICAL MONITORING

There is a certain problem in ecological monitoring of the environment state according to the measured values of a certain abiotic factor. Namely, how to build a continuous map of environmental pollution throughout the controlled area, based on the results of measurements carried out at a finite number of points inside the controlled territory. The aim of the work is to study the possibility of using the method of self organizing neural maps (SOM) for the problems of the ecological monitoring of the environment, and specifically for building an accurate continuous map of environmental pollution on the ground. The materials and methods of researches are the results of measurements the ambient equivalent of the continuous X-ray and gamma radiation dose rate on a territory of the historical center of Kharkiv has been used as research materials; processing of the obtained data by SOM's methods using MatLab 8.1 and STATISTICA 10 computer programs has been done. Results: in the process of 1000 self-learning cycles of a neural network of 100 initial active neurons randomly located on the controlled area map, 25 neural clusters have been obtained, the coordinates of the centers of which practically coincided with the 25 control points coordinates. A continuous map of the background radiation on the controlled area has been built. The accuracy of this map was no worse than 0.25 μR/hour. Conclusions: the possibility of using the SOM methods to build a continuous map of the level of environmental pollution on the ground based on the results of measuring the values of a certain abiotic factor in a finite number of points has been proven. It has been proven that this method is more accurate compared to the methods of regression mapping and cluster analysis, from which it is essentially different. The possibilities for a significant improvement in the accuracy of the method lie in increasing the number of initial neurons on the terrain map and the number of iterations during their training.