Predictive Analytics of Air Alerts in the Russian-Ukrainian War
This provides a domain-specific tool for forecasting air alerts in conflict zones, though it is incremental as it applies existing predictive analytics methods to new war data.
The paper tackled predicting air alerts during the Russian-Ukrainian war by analyzing correlations and geospatial patterns, finding that alerts depend on adjacent regions and seasonality features, enabling feasible predictive models.
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.