MODELING THE SUSTAINABLE DEVELOPMENT OF THE UKRAINIAN REGIONS BY NEURAL NETWORKS

The article is devoted to the issue of modeling sustainable development of regions. In the article, the author notes that the issue of modeling sustainable development of regions has long been engaged in both domestic and foreign scientists. However, this topic is still relevant today. The paper emphasizes the importance of preserving the ecological state of the country in general and at the regional level in particular. It also emphasizes the need for harmonious development of social, economic and environmental spheres. Literary analysis of scientific works in the direction of research also leads the work of researchers and approaches to modeling the sustainable development of regions that were presented several decades later. The author gives a brief overview of the models that have already been proposed by researchers and notes the advantages and disadvantages of these models. Summarizing the analysis of literature research, the paper identifies a number of problems that are still unresolved in modeling sustainable development of regions. The paper aims to eliminate the existing contradictions in the modeling of sustainable development and proposed an alternative approach to modeling which is based on establishing a reliable relationship between the main indicators of the region in three areas: economic, environmental and social. In accordance with this goal, the author hypothesized the possibility of using the tool of neural networks in order to form reliable links between indicators of sustainable development and the implementation of further modeling. Thus, the paper presents arguments in the direction of using neural networks to achieve the goals. In order to build a neural network, the author formed a system of input and output parameters in three areas: economic, social and environmental. In selecting the factors, the author relied on his previous published study in which a correlation analysis of sustainable development factors was conducted, and the most influential ones were selected. The basis for the training of neural networks were statistical data on the sustainable development of Ukraine from 2004 to 2018. The construction of three neural networks: economic, social and environmental spheres. Only 70 percent of the sample data was used to train the networks, and the rest was used for testing. As a result, the constructed neural networks showed a high degree of forecast quality and can be further successfully used to model indicators of sust