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Predictive Analytics of Air Alerts in the Russian-Ukrainian War

Demian Pavlyshenko, Bohdan Pavlyshenko

TL;DR

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 to show that the alert status in a particular region is highly dependable on the features of its adjacent regions.

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.

Predictive Analytics of Air Alerts in the Russian-Ukrainian War

TL;DR

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 to show that the alert status in a particular region is highly dependable on the features of its adjacent regions.

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.

Paper Structure

This paper contains 7 sections, 34 figures.

Table of Contents

  1. Introduction
  2. Exploratory data analysis
  3. Training and validation of machine learning model
  4. Conclusion
  5. Appendix
  6. Forecasting alerts with time horizon 15 minutes
  7. Python function for training and validation of machine learning model

Figures (34)

  • Figure 1: Heatmap for the total duration of air alerts in Ukrainian regions (minutes)
  • Figure 2: Heatmap for regions' total duration of air alerts which occurred simultaneously with Kharkiv oblast (minutes)
  • Figure 3: Time series for daily median for alert durations by regions (minutes)
  • Figure 4: Boxplots for the duration of alerts by regions (minutes)
  • Figure 5: Boxplots for the total duration of air alerts per day by regions (minutes)
  • ...and 29 more figures