The paper considers exploratory data analysis and approaches in predictive analytics for air alerts during the Russian-Ukrainian war which broke out on Feb 24, 2022. The results illustrate that alerts in regions correlate with one another and have geospatial patterns which make it feasible to build a predictive model which predicts alerts that are expected to take place in a certain region within a specified time period. The obtained results show that the alert status in a particular region is highly dependable on the features of its adjacent regions. Seasonality features like hours, days of a week and months are also crucial in predicting the target variable. Some regions highly rely on the time feature which equals to a number of days from the initial date of the dataset. From this, we can deduce that the air alert pattern changes throughout the time.
@article{arxiv.2411.14625,
title = {Predictive Analytics of Air Alerts in the Russian-Ukrainian War},
author = {Demian Pavlyshenko and Bohdan Pavlyshenko},
journal= {arXiv preprint arXiv:2411.14625},
year = {2024}
}