Forecasting the Degree of Urbanization Using an Artificial Neural Network Model

Abstract

This research solves the task of forecasting and analyzing the dynamics of the urban population share using an artificial neural network method, taking the Samarkand region as an example. Based on the official data of the State Statistics Committee of the Republic of Uzbekistan, 25 significant factors affecting the degree of urbanization were identified. Then, an artificial neural network model was created. Using this model, forecasts of changes in the urban population share of the Samarkand region for 2023-2025 were made. The obtained results contain data that can be practically applied for managing and regulating urbanization processes.