Improvement of migration balance forecasting within the framework of management of socio-economic development of single-industry towns on the basis of artificial intelligence (on the materials of the Republic of Kazakhstan)

Abstract

In this study, models of artificial neural networks of migration balances are developed in order
to improve the efficiency of management of socio-economic development of single-industry towns
in the Republic of Kazakhstan. Now there are no universal tools for forecasting indicators of socioeconomic development in general and characterising migration processes in particular. However, the
volume of budget allocations to address human resources issues in single-industry towns, the creation
of social facilities and the implementation of other activities that are significant for economic, social
and infrastructural development, the direction of development of single-industry towns depend on the
forecast of migration balance. In addition, the forecast of migration balance is important for identifying
core areas and their subsequent priority development. In this article, a substantial analysis of researchers’
works is carried out, and it is determined that artificial intelligence models, in particular, the most
adaptive neural networks are not used in forecasting the migration balance. The purpose of this study
is to develop models of artificial neural networks of migration balance to improve the efficiency of
management of socio-economic development of single-industry towns in the Republic of Kazakhstan.
The result of the research is a methodological approach and a toolkit for forecasting the migration
balance for single-industry towns in the Republic of Kazakhstan. The developed approach to forecasting
and the toolkit are universal in the field of forecasting socio-economic indicators. In addition, the results
described in the article can be used in other studies in the field of forecasting and planning. In particular,
the developed toolkit can be used to assess the effectiveness of management decisions, for example, in
the implementation of evidence-based policy for the development of single-industry towns.