Proceedings of the International Conference on Social Sciences
Year: 2024
DOI:
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Prediction of Extraordinary Events Caused by Socioeconomic Aspect Using the Random Forest Method
Jozef Surkovsky
ABSTRACT:
In forecasting extraordinary events, we now see socioeconomic factors playing an increasingly crucial role, alongside environmental and industrial ones, in influencing the likelihood of these occurrences. Socioeconomic variables such as unemployment, income inequality, educational levels, and urbanization patterns can shape individual and community responses in crisis situations. Understanding these influences and incorporating them into predictive models is essential for reducing the adverse effects of extraordinary events on society. This article emphasizes the value of socioeconomic data in predictive analytics, particularly through the application of the Random Forest method. By analyzing the dynamic relationships and interactions among various socioeconomic factors that impact society in connection with extraordinary events, we uncover how these factors heighten the probability of such occurrences. Random Forest stands out in its ability to identify complex interdependencies among variables, ultimately providing a robust model capable of delivering early warnings. Testing the established hypothesis with Random Forest has proven its effectiveness in identifying the key socioeconomic factors and uncovering their interactions. The results indicate a statistically significant impact of socioeconomic factors on the prediction of extraordinary events, underscoring the need to integrate such data for enhanced accuracy in predictive modeling. This study not only contributes valuable insights into the field of extraordinary event prediction but also opens a new, promising pathway for leveraging socioeconomic data to better foresee and mitigate the effects of these events.
keywords: Data analysis; Crisis situations; Modeling; Socioeconomic factors; Statistical significance