Predictive Analytics of Air Alerts in the Russian-Ukrainian War

TL;DR

This paper analyzes and models air alert patterns during the Russian-Ukrainian war, revealing geospatial and temporal dependencies that enable accurate prediction of alerts in specific regions over time.

Contribution

It introduces a predictive model that captures spatial and temporal patterns of air alerts, leveraging geospatial correlations and seasonality features.

Findings

Alerts in regions are correlated and exhibit geospatial patterns.

The alert status depends heavily on neighboring regions' features.

Seasonality factors like hours, days, and months are crucial for prediction.

Abstract

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.