Estimating the level of urbanization based on machine learning

Abstract

In this study, the problem of forecasting and analyzing the urbanization process using machine learning methods was solved using the example of Samarkand region. The official data of the State Statistics Committee of the Republic of Uzbekistan was used, and 25 important features affecting the level of urbanization were identified. Subsequently, various machine learning models were built, and their effectiveness was compared, with the artificial neural network model showing the highest result. With the help of this model, the levels of urbanization in Samarkand region for 2023-2025 were predicted. The obtained results are of practical importance for managing and regulating urbanization processes.

Topics

Identifiers

DOI

10.1117/12.3036578

Citations and references