Investigation of the causes of emergencies based on official statistics

Roman Kovalenko

National University of Civil Defence of Ukraine

https://orcid.org/0000-0003-2083-7601

Andrii Kalynovskyi

National University of Civil Defence of Ukraine

https://orcid.org/ 0000-0002-1021-5799

Maxim Zhuravskij

National University of Civil Defence of Ukraine

https://orcid.org/0000-0001-8356-8600

Valeriya Kashchavtseva

Emergency Service of Ukraine in Kharkоv region

https://orcid.org/0000-0002-1584-4754

DOI: https://doi.org/10.52363/2524-0226-2021-33-8

Keywords: emergency, fire, statistical data, correlation, predictive model

Abstract

The article examines the relationship between the official indicators of demographic, social and economic statistics and the number of emergencies that occur on the territory of the regions of Ukraine. The following were selected as indicators of demographic, social and economic statistics: population size; education and waste management; total area of the housing stock; sown area of grain and leguminous crops; real disposable income, as a percentage of the corresponding period of the previous year. The relationship between these indicators was checked by conducting a correlation analysis. In 56% of the studied cases between the total number of emergencies and the indicator of the population living in the territory of the regions, and in the city of Kiev, there is an average and high strength of the correlation. Between the other indicators of demographic, social and economic statistics analyzed in the work and the total number of emergencies, there were significantly fewer cases of detection of medium and high strength of correlations. The reason for obtaining negative values of the correlation coefficients between the total number of emergencies and the indicator of generation and waste management is not clear. At the same time, the numerical value of the correlation coefficients makes it possible to assert about the average and high strength of correlations. This is likely due to the small sample size. The established relationship between the indicators of the total number of emergencies and the population size was described by a linear regression equation. The adequacy of the regression model was checked by Fisher's criterion, and provides a correlation coefficient of at least 0,7, which confirms the reliability of the developed mathematical model.

References

1. Alfonso, G-M. (2020). A GIS-physically-based emergency methodology for predicting rainfall-induced shallow landslide zonation. Geomorphology, 359, 107121. doi: 10.1016/j.geomorph.2020.107121
2. Leonardo, N. F., Didier, A.V-O, Liang, Z., Manoel, F. C., Elbert, E. N. M. (2020). Global fire season severity analysis and forecasting. Computers & Geosciences, 134, 104339. doi: 10.1016/j.cageo.2019.104339
3. Pham, B. T., Jaafari, A., Avand, M., [et al.]. (2020). Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction. Symmetry, 12, 1022. doi: 10.3390/sym12061022
4. George, E. S., Imad, H. E., George, M. (2011). Efficient forest fire occurrence prediction for developing countries using two weather parameters. Engineering Appli-cations of Artificial Intelligence, 24, 888‒894. doi: 10.1016/j.engappai.2011.02.017
5. Volkan, S., Omer, K., Merih, G. (2020). A Bayesian network model for predic-tion and analysis of possible forest fire causes. Forest Ecology and Management, 457, 117723. doi: 10.1016/j.foreco.2019.117723
6. Guangyin, J., Qi. W., Cunchao, Z., Yanghe, F., Jincai, H., Xingchen, H. (2020). Urban Fire Situation Forecasting: Deep sequence learning with spatio-temporal dynamics. Applied Soft Computing, 97, 106730. doi: 10.1016/j.asoc.2020.106730
7. Mohsen, Y., Seyyed, A. H., Mahdi, F. (2021). Spatiotemporally explicit earth-quake prediction using deep neural network. Soil Dynamics and Earthquake Engineer-ing, 144, 106663. doi: 10.1016/j.soildyn.2021.106663
8. Rebecca, E., Hannah, C., Andrea, F., [et al.]. (2020). Emergency flood bulletins for Cyclones Idai and Kenneth: A critical evaluation of the use of global flood forecasts for international humanitarian preparedness and response. International Journal of Disaster Risk Reduction, 50, 101811. doi: 10.1016/j.ijdrr.2020.101811
9. World Fire Statistics. Report № 25. Retrieved from http://www.ctif.org/sites/default/files/2020-06/CTIF_Report25.pdf [in English].
10. Informacijno-anality`chna dovidka pro vy`ny`knennya NS v Ukrayini upro-dovzh 2019 roku. Retrieved from https://www.dsns.gov.ua/ua/Dovidka-za-kvartal/103179.html [in Ukrainian].