Information System to Analyze Human Environmental Wellbeing for Assessing and Reducing Technogenic Risk

Abstract

Abstract The article provides a rationale for the need of developing the information system to analyze human environmental wellbeing (HEW) on the basis of artificial neural networks. The application of artificial neural networks is pre-conditioned by the need of prompt analysis and processing to take decisions on the implementation of environment protection measures for large data arrays accumulated as a result of environmental monitoring. The suggested information system for technogenic risk assessment implements a multi-limit way to control the state of a multi-parametric object and allows assessing human environmental wellbeing in real time. The authors provide structural charts for the information system to assess technogenic risk and the block to process and store information on the basis of an artificial neural network LVQ (Learning Vector Quantization). The authors provide an algorithm and the results for the artificial neural network LVQ training.