Predictive Analytics of Air Alerts in the Russian-Ukrainian War

• URL: http://arxiv.org/abs/2411.14625v1
• Date: Thu, 21 Nov 2024 22:58:39 GMT
• Title: Predictive Analytics of Air Alerts in the Russian-Ukrainian War
• Authors: Demian Pavlyshenko, Bohdan Pavlyshenko,
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• A Spatiotemporal Radar-Based Precipitation Model for Water Level Prediction and Flood Forecasting [0.9487148673655145]
  In July 2017, the cities of Goslar and G"ottingen experienced severe flood events characterized by short warning time of only 20 minutes. This highlights the critical need for a more reliable and timely flood forecasting system.
  arXiv  Detail & Related papers  (2025-03-25T10:14:54Z)
• FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere [53.22497376154084]
  We propose FengWu-Weather to Subseasonal (FengWu-W2S), which builds on the FengWu global weather forecast model and incorporates an ocean-atmosphere-land coupling structure along with a diverse perturbation strategy. Our hindcast results demonstrate that FengWu-W2S reliably predicts atmospheric conditions out to 3-6 weeks ahead, enhancing predictive capabilities for global surface air temperature, precipitation, geopotential height and intraseasonal signals such as the Madden-Julian Oscillation (MJO) and North Atlantic Oscillation (NAO) Our ablation experiments on forecast error growth from daily to seasonal timescales reveal potential
  arXiv  Detail & Related papers  (2024-11-15T13:44:37Z)
• Generating Fine-Grained Causality in Climate Time Series Data for Forecasting and Anomaly Detection [67.40407388422514]
  We design a conceptual fine-grained causal model named TBN Granger Causality. Second, we propose an end-to-end deep generative model called TacSas, which discovers TBN Granger Causality in a generative manner. We test TacSas on climate benchmark ERA5 for climate forecasting and the extreme weather benchmark of NOAA for extreme weather alerts.
  arXiv  Detail & Related papers  (2024-08-08T06:47:21Z)
• Spatial-temporal Forecasting for Regions without Observations [13.805203053973772]
  We study spatial-temporal forecasting for a region of interest without any historical observations. We propose a model named STSM for the task. Our key insight is to learn from the locations that resemble those in the region of interest.
  arXiv  Detail & Related papers  (2024-01-19T06:26:05Z)
• Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes [4.324839843326325]
  We develop more reliable methods for uncertainty in neural TPP models via the framework of conformal prediction. A primary objective is to generate a distribution-free joint prediction region for an event's arrival time and mark, with a finite-sample marginal coverage guarantee.
  arXiv  Detail & Related papers  (2024-01-09T15:28:29Z)
• Association rule mining with earthquake data collected from Turkiye region [0.0]
  This study presents the most prominent association rules for the earthquakes recorded in Turkiye region in the last 5 years. Results indicate statistical inference with events recorded from regions of various distances.
  arXiv  Detail & Related papers  (2023-12-26T18:36:01Z)
• Performative Time-Series Forecasting [71.18553214204978]
  We formalize performative time-series forecasting (PeTS) from a machine-learning perspective. We propose a novel approach, Feature Performative-Shifting (FPS), which leverages the concept of delayed response to anticipate distribution shifts. We conduct comprehensive experiments using multiple time-series models on COVID-19 and traffic forecasting tasks.
  arXiv  Detail & Related papers  (2023-10-09T18:34:29Z)
• Joint Forecasting of Panoptic Segmentations with Difference Attention [72.03470153917189]
  We study a new panoptic segmentation forecasting model that jointly forecasts all object instances in a scene. We evaluate the proposed model on the Cityscapes and AIODrive datasets.
  arXiv  Detail & Related papers  (2022-04-14T17:59:32Z)
• Deep Autoregressive Models with Spectral Attention [74.08846528440024]
  We propose a forecasting architecture that combines deep autoregressive models with a Spectral Attention (SA) module. By characterizing in the spectral domain the embedding of the time series as occurrences of a random process, our method can identify global trends and seasonality patterns. Two spectral attention models, global and local to the time series, integrate this information within the forecast and perform spectral filtering to remove time series's noise.
  arXiv  Detail & Related papers  (2021-07-13T11:08:47Z)
• A generative adversarial network approach to (ensemble) weather prediction [91.3755431537592]
  We use a conditional deep convolutional generative adversarial network to predict the geopotential height of the 500 hPa pressure level, the two-meter temperature and the total precipitation for the next 24 hours over Europe. The proposed models are trained on 4 years of ERA5 reanalysis data from 2015-2018 with the goal to predict the associated meteorological fields in 2019.
  arXiv  Detail & Related papers  (2020-06-13T20:53:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.