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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2411.14625 |
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| _version_ | 1866912130221998080 |
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| author | Pavlyshenko, Demian Pavlyshenko, Bohdan |
| author_facet | Pavlyshenko, Demian Pavlyshenko, Bohdan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_14625 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Predictive Analytics of Air Alerts in the Russian-Ukrainian War Pavlyshenko, Demian Pavlyshenko, Bohdan Machine Learning Artificial Intelligence Computers and Society 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. |
| title | Predictive Analytics of Air Alerts in the Russian-Ukrainian War |
| topic | Machine Learning Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2411.14625 |