Victor Ilin

Work place: Yuriy Fedkovych Chernivtsi National University, Chernivtsi, 58012, Ukraine



Research Interests:


Viktor Ilin graduated from Yuriy Fedkovych Chernivtsi National University, Chernivtsi. Currently, he is PhD student of Technical Sciences at Yuriy Fedkovych Chernivtsi National University. His research interests include data mining, information technology for decision support, swarm intelligence systems, and industry-specific geographic information systems.

Author Articles
Modelling of an Intelligent Geographic Information System for Population Migration Forecasting

By Dmytro Uhryn Yuriy Ushenko Vasyl Lytvyn Zhengbing Hu Olga Lozynska Victor Ilin Artur Hostiuk

DOI:, Pub. Date: 8 Aug. 2023

A generalized model of population migration is proposed. On its basis, models of the set of directions of population flows, the duration of migration, which is determined by its nature in time, type and form of migration, are developed. The model of indicators of actual migration (resettlement) is developed and their groups are divided. The results of population migration are described, characterized by a number of absolute and relative indicators for the purpose of regression analysis of data. To obtain the results of migration, the author takes into account the power of migration flows, which depend on the population of the territories between which the exchange takes place and on their location on the basis of the coefficients of the effectiveness of migration ties and the intensity of migration ties. The types of migration intensity coefficients depending on the properties are formed. The lightgbm algorithm for predicting population migration is implemented in the intelligent geographic information system. The migration forecasting system is also capable of predicting international migration or migration between different countries. The significance of conducting this survey lies in the increasing need for accurate and reliable migration forecasts. With globalization and the connectivity of nations, understanding and predicting migration patterns have become crucial for various domains, including social planning, resource allocation, and economic development. Through extensive experimentation and evaluation, developed migration forecasting system has demonstrated results of human migration based on machine learning algorithms. Performance metrics of migration flow forecasting models are investigated, which made it possible to present the results obtained from the evaluation of these models using various performance indicators, including the mean square error (MSE), root mean square error (RMSE) and R-squared (R2). The MSE and RMSE measure the root mean square difference between predicted and actual values, while the R2 represents the proportion of variance explained by the model.

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