Developing Risk Assessment Model for Altering Conditions of Forest Reserves in an Oil-Production Region

The scientific problem, the solution of which is aimed at this work, is the implementation of a systematic method of assessing and predicting the influence of anthropogenic impact on the natural environment of the oilproducing region. The significance of the research is confirmed by the absence of developed and enacted criteria for determining risks and finding techniques of their assessment, especially those which consider the specificity of the oil-producing region. A special feature of the proposed approach is the use of hybrid methods of machine learning in conjugation with the spatial information analysis on the basis of the history of incidents occurred in the forest reserves of the region. Some statistical estimates of the influence of the assessed factors of risk formation on an emergence probability of an incident have been obtained. Within the framework of this study, a vectorbased description of signs of incidents was formulated and validated followed by a forecast based on methods of machine learning. Developed models have a wide range of application and may become a framework for creating assessments of risk factors of human-caused impacts on the natural environment, including oil spills, illegal forest felling, unauthorized dumps of household and construction waste, etc. Achievement of the established goal will allow to rank risks in the forest reserves of the region, develop a model for the assessment of these risks, visualize by means of geo-information technologies for monitoring loads imposed by planned inspections by controland-supervision authorities on the region’s enterprises, as well increase the efficiency of the detection of violations. Keywords–data analysis, machine learning, neural networks, spatial analysis, geographic information systems, risk-based approach