=Paper=
{{Paper
|id=Vol-2805/paper6
|storemode=property
|title=Assessing Losses of Human Capital Due to Man-Made Pollution Caused by Emergencies
|pdfUrl=https://ceur-ws.org/Vol-2805/paper6.pdf
|volume=Vol-2805
|authors=Myroslava Bublyk,Victoria Vysotska,Yurii Matseliukh,Volodymyr Mayik,Mariya Nashkerska
|dblpUrl=https://dblp.org/rec/conf/citrisk/BublykVMMN20
}}
==Assessing Losses of Human Capital Due to Man-Made Pollution Caused by Emergencies==
https://ceur-ws.org/Vol-2805/paper6.pdf
Assessing Losses of Human Capital Due to Man-Made
Pollution Caused by Emergencies
Myroslava Bublyk1[0000-0003-2403-0784], Victoria Vysotska2[0000-0001-6417-3689], Yurii
Matseliukh3[0000-0002-1721-7703], Volodymyr Mayik4[0000-0002-6650-2703], and Mariya
Nashkerska5[0000-0003-1432-5829]
1-3,5
Lviv Polytechnic National University, Lviv, Ukraine,
4
Ukrainian Academy of Printing, Lviv, Ukraine
1my.bublyk@gmail.com, 2Victoria.A.Vysotska@lpnu.ua,
3indeed.post@gmail.com, 4 maik@mail.uad.lviv.ua,
5nashkerska@gmail.com
Abstract. World-renowned approaches to assessing losses of the human capital
due to man-made pollution caused by emergencies have been studied and com-
pared. The concept of the approach to assessing losses of the human capital due
to man-made pollution was built on the theory of fuzzy sets. The proposed con-
cept of the approach assessing losses of the human capital due to man-made
pollution is based on the theory of fuzzy sets. A qualitatively new approach to
the economic assessment of possible (predicted) losses of human capital caused
by pollution from emergencies is proposed. Pollution is considered as a set of
emissions into the atmosphere, discharges into water, pollution of land with liq-
uid and solid industrial waste, etc. This was taken into account when choosing a
data set to test the concept of the proposed approach. It takes into account the
risks of possible losses from mortality using fuzzy logic. The expediency of ap-
plying the theory of fuzzy sets to calculate the mortality rate due to pollution
from emergencies is substantiated.
Keywords: Risk, Assessing Losses, Risks of Possible Losses, Human Capital,
Fuzzy Sets Theory, Man-Made Pollution, Emergencies.
1 Introduction
The problem of risk management is directly related to the problem of the calculations
of man-made damage. The largest share of economic losses falls on the damage to the
population caused by associated with its morbidity, premature ageing, and mortality,
and as a result - losses from underutilization of labor resources. Mortality in working-
age leads to great losses for society. A person begins to create public goods only after
the age of 28 until the end of his period of economically active age because otherwise
a person only pays for the money spent on it. The death of a person under the age of
28 is considered a direct economic loss, and after that age - indirect. Investigation of
Copyright © 2020 for this paper by its authors. This volume and its papers are published under
the Creative Commons License Attribution 4.0 International (CC BY 4.0).
the structure of economic losses inflicted on the population caused by man-made
pollution and assessment of the size of these losses become acute relevance in terms
of the spread of the disease in CoVid 2019. Increased mortality due to emergencies
caused by pandemic CoVid 2019, forced to create new approaches to evaluating eco-
nomic losses of human capital.
The unpredictability of emergencies and the lack of the necessary information to
assess possible losses (expected losses) lead to the existence of uncertainty, the nega-
tive manifestations of which are commonly described as risks. The study of the appli-
cation of fuzzy set theory to predict the risks of possible losses of human capital due
to an emergency is fragmentary. For this reason, the main purpose of the work is to
use fuzzy logic to develop a concept of methodology for assessing the loss of human
capital due to man-made pollution caused by emergencies. Achieving the goal in-
volves testing the proposed approach to assessing the loss of human capital due to
man-made pollution caused by emergencies, using the available data set in open
sources of the State Statistics Service of Ukraine.
2 Related Works
Problems of assessment of human capital and its changes caused by emergencies, in
scientific research [1, 2] are considered by scientists from different points of view and
are interdisciplinary. Today, information technology and systems play a crucial role in
ensuring administrative and economic management [3, 4], so researchers pay special
attention to the problems of analysis of causes [5] and assessment of the consequences
of emergencies (EM) [6]. To do this, they are grouped and systematized in the form of
interactive databases [7-11].
The most well-known international databases and computer systems for calculating
losses from man-made and natural disasters are the following Emergency Manage-
ment Australia Disaster Database [7], Canadian Disaster Database [8], Em-Dat [9],
NatCat [10], and Sigma [11]. The first two NA databases are maintained by govern-
ment organizations, including the governments of Australia and Canada. They are
available online and contain information on all emergencies in Australia since 1622
(Emergency Management Australia Disaster Database) [7], and on all emergencies in
Canada since 1900 (Canadian Disaster Database) [8].
Among the private international databases of the National Assembly, the most fa-
mous are NatCat (Munich Reinsurance Company) [10], and Sigma (Swiss Reinsur-
ance company) [11]. NatCat is run by one of the German insurance companies Mu-
nich Reinsurance Company. The information in this database applies only to natural
disasters. There is information on more than 25,000 natural disasters, including the
largest international disasters and accidents. Here, approximately 1000 NA is added
annually [10]. Sigma is run by the Swiss insurance company Swiss Reinsurance com-
pany. This database contains information on natural and man-made emergencies that
have caused significant damage. The database includes only those emergencies that
caused damage according to strict criteria. The criteria are 20 deaths and / or 50 inju-
ries, 2,000 homeless and / or insured losses of at least $ 14 million. US (in the event
of a water disaster), $ 28 million. US (in the event of an air disaster) and $ 35 million.
USA (for other NC). This database contains information on about 10,000 cases. Al-
most 300 NA is added every year [11].
Such databases are common in many countries, as they allow the insurance com-
pany to predict and characterize the insured event for which the payment will be
made. According to the insurance policy of each of the companies, the criteria for
assessing an emergency also differ. Private databases are not available to the average
user. Insurance companies-owners annually publish bulletins with statistics and up-
dates of records in it.
EM-DAT is the largest international database of emergencies, which contains data
on almost 22,000 emergencies [9]. It provides complete information on losses and
damages caused by both natural and man-made disasters. The sources of information
for this database are various governmental and non-governmental organizations, in-
cluding the UN, insurance agencies that practice insurance of relevant risks, research
institutes, and the media [9]. EM-DAT is the largest database of emergencies, but it
does not provide enough information on man-made disaster losses.
The loss of human resources due to emergencies is a key factor in determining the
scale (size) of emergencies and their classification in these international databases of
disasters and accidents. This indicator is a traditional economic indicator that reflects
the negative impact of emergencies on the economy of states.
Losses of human capital have a negative impact on the national economies of states
in the short term (immediately after the onset of emergencies) through the payment of
compensation for losses and losses. The loss of human capital has a destructive effect
on the resulting characteristics of national economies and in the long run due to the
shortfall in the formation of GNP regions and states.
Losses among the population cause losses in future periods due to losses of intel-
lectual capital. Such losses go beyond the local boundaries of regions and states and
are global in nature. Many authors [1, 12, 13] have devoted their research to the de-
velopment of security at potentially dangerous sites. Other authors [14, 15, 16, 17]
focus on the study of human capital losses in critical facilities, where man-made
emergencies are predicted with high probability.
For potentially dangerous objects, various complex security systems have been de-
veloped according to a wide set of criteria "cost - security - benefit" [1, 12, 15, 18],
"threat - loss - damage" [6, 14, 19], systems for taking into account the impact of
natural hazards on them [6], systems for taking into account the impact of man-made
emergencies on these potentially dangerous objects, the environment, population,
economy [1, 18] and many other types of systems or techniques [ 17, 19, 20].
Most of these information systems and known methods determine the impact of
emergencies in the short term [1, 3, 5, 21]. The authors [5, 14, 22] assess the direct
loss of human capital due to an emergency. These losses of human capital are called
direct losses caused by emergencies.
Man-made emergencies are accompanied by environmental pollution in most cas-
es. Damage to the environment, in most scientific sources [12, 19, 23] is called envi-
ronmental and economic damage and is also classified as direct damage. Emissions of
harmful substances into the atmosphere, discharges of polluted waters into rivers and
reservoirs, pollution of land and forest areas with hazardous substances degrade the
quality of the environment. Such damage to the environment leads to a deterioration
in the quality of life in the long run. This is manifested in an increase in the incidence
of the population and an increase in mortality from these diseases. All these losses
from emergencies are classified as indirect losses [1, 17, 22]. The problem of estimat-
ing the loss of human capital due to man-made pollution caused by emergencies re-
mains unresolved.
3 Materials/Methods/Methodology
The orthodox methodology to assessing losses of the population due to emergencies is
determined by the official recommendations of the "Methodology for assessing losses
from the consequences of man-made and natural emergencies" in Ukraine [21]. Ac-
cording to these recommendations [21], the formula for the total man-made losses
from the consequences of emergencies (TML) is:
TML = social losses + environmental losses + economic losses
The social losses (SL) are defined as the damage from the loss of human life and
health from the consequences of emergencies. It is accounted as the sum of losses
from the disposal of labor resources from production; funeral expenses and payment
costs and pensions in case of loss of a breadwinner. The social losses due to the emer-
gencies consequences are defined as follows:
SL = Ldlr + ∑Cpfa + ∑Cppb
where Ldlr is losses from the disposal of labor resources from production; Cpfa is the
cost of paying funeral assistance; Cppb is the cost of paying pensions in the event of
loss of a breadwinner.
The losses from the disposal of labor resources from production are defined as fol-
lows:
Ldlr = Мl×Nl + Мt×Nt + Мі×Nі + Мz×Nd
where Мl is losses from a minor accident; Мt are severe case losses; Мі is losses from
a person's disability; Мz is losses from death.
Necessary data for calculation are the number of employees within the territory af-
fected by the emergency situation who lost their ability to work for up to 9 days (Nl),
more than 9 days (Nt), became disabled (Ni), died (Nd).
The costs for the payment of funeral assistance are defined as follows:
Cpfa = 12 × Мfa × Nd
where Мfa is funeral allowance (according to social security authorities); Nd is the
number of deaths due to the emergency.
The cost of paying pensions in the event of loss of a breadwinner due to the emer-
gencies consequences are defined as follows:
Cppb = 12 × Mmp × (18 – Ac)
where Mmp is the amount of the monthly pension for a child under the age of majori-
ty 18 years (according to the social security authorities); Ac is age of the child.
Necessary data for calculation are the number and age of disabled family members
who died as a result of the emergency.
The basis for the systematization of man-made losses should be the calculation of
losses amount based on the definition of factor-by-factor and to-recipient losses [1,
21]. Factor-by-factor losses reflect a comprehensive economic assessment of the
damage caused by the main factors of influence [19-24]. These include damage to the
population from air pollution, surface and groundwater, land, and soil [5, 14, 21]. To-
recipient losses reflect an economic assessment of the actual damage caused to the
main recipients of the impact [21]. Due to the peculiarities of the dynamics and im-
pact of emergencies on the human capital [1, 17, 19, 21, 22], the formula for calculat-
ing the total amount of losses, determined by this method, is imperfect and needs to
be supplemented.
In the event of an emergency, there is an environmental risk, due to which it is ac-
cepted to calculate the environmental damage. To determine the risk index R, the
authors of [19] propose to use the sum of the products of the probability of loss of the
i-th species pi and the magnitude of possible losses of the same i-th species Qi for the
whole set of m species losses:
m
R= ∑ pQ .
i i
i =1
The authors of [19] substantiate that the theory of probability is a partial case of the
theory of fuzzy sets, because here they operate with the functions of fuzzy affiliations
to the distribution of possibilities in fuzzy sets. This proves the greater possibilities of
fuzzy set theory compared to the classical probabilistic approaches for assessing loss-
es of the human capital due to emergencies. The authors of [19] used the possibilities
of fuzzy logic to build a heterodox approach to assess possible losses in production by
assessing the risks of possible losses.
4 Experiment/Results/Discussions
To construct a new approach for assessing losses of the human capital due to man-
made pollution, we use the basic principles and laws of fuzzy sets theory. The pro-
posed approach uses of fuzzy-logical result at the following its stages (Fig. 1).
Fuzzy subset F of the set of Р factors of the losses is determined by the member-
ship function µF(р), where p - an element of the universal set, that is р ∈ Р. The func-
tion of reflecting elements of the set P on the set of numbers in the range [0, 1], which
characterize the degree of membership of each element р ∈ Р to a fuzzy set F, where-
by F ⊂ Р. If the full set P covers finite number of sets of elements р1, р2,…..,рn, then
the fuzzy subset F can be represented as F =∑ µF(pі)/pі according to the [19, 25].
In our case, we research pollutions influences on the human capital, which are de-
scribed at the input n variables х1, х2, … хn, and variable у is an output (the level of
technogenic losses, or technogenic loss ratio) by y=fy(х1, х2, … хn).
Identification of
Risk sour-
emergency Assessing of impacts
ces identi- Assessing the risk
and opportunities reactions Uncertainty assessing
fication acceptability
Identification of risk
factors relations Application of
fuzzy-logical result
Risk
recipients Assessing probability of Assessing the probability
finding potential losses of possible states
State of emergency at t0
Identification of the Losses analysis
human capital Human Analysis of alternative
capital states
Analysis of the
viability of
Identifying dam- human capital
age factors relations Application of
fuzzy-logical result
Analysis of
Resources the set of
target factors
Costs analysis and losses analysis of
Hum. capital state at t0 alternative states
Comparison
Human capital state at t1
Application of
Comparison of the altered state of human capital as a viable fuzzy-logical result
The decision-making on the need for regulation of human capital
Formation of regulating actions
Formation of regulating actions, Application of
activities, projects and programs to Optimization of action,
fuzzy-logical result
achieve the objectives effectiveness evaluating
Regulation
Monitor and control the changes
of human capital Application of Achieving targets for the
fuzzy-logical result human capital
Fig. 1. Conceptual model of the application of fuzzy-logical result in new approach for as-
sessing losses of the human capital due to man-made pollution
To solve the problem assessing losses of the human capital due to man-made pollu-
tion it is necessary to develop a method of fuzzy decision making as a result of all the
stages of fuzzy-logical conclusion (see Figure 1). The expert analyst creates a separate
set of parameters X = {хі}, where і = 1, …, N, which is the indicator for assessing
losses of the human capital due to man-made pollution (otherwise the index of risk of
emergency pollution). In formalized language, that means to a fixed vector of input
variables Х→ =〈х1→, х2→,….., хn→〉, хі→ ∈ Рі it should be definitely put in correspond-
ence a solution y→ ∈ Y (for an object with a discrete output) according to the [19].
The expert analyst must choose values according to their significance for assessing
losses of the human capital due to man-made pollution; take into account various
sources of losses and others. Thus, the set of all indices to assess complex measure of
losses may include both qualitative and quantitative criteria and х1, х2, … хn [19].
Fuzzy descriptions of the structure of the new method assessing losses of the hu-
man capital due to emergency pollution occur in connection with the expert’s uncer-
tainty arising during the various classifications. The expert creates a linguistic varia-
ble with its term-set values.
The losses level of human capital due to man-made pollution can be described as
term-set of values {Very Low, Low, Medium, High, Very High} [19]. Then the expert
chooses the corresponding quantitative trait for a linguistic variable y, which is losses
of the human capital due to man-made pollution. In our case we used trapezoidal
membership functions. The next stage is to construct the fuzzy knowledge base by the
principle of "if-then". In our case, it is the level of losses of the human capital due to
man-made pollution.
The stage of fuzzy decision-making, in our case, is done with the help of a table of
decision rules for the system of fuzzy knowledge from the knowledge base [19]. The
stage of transformation of fuzzy data from the output stage of fuzzy decision into
precise quantities (quantitative or qualitative) used in the economic environment, is
done in the opposite direction to the stage fuzzyfication for linguistic variable of the
damage level Y.
To test the new method of assessing losses of human capital due to emergency
pollution, a data set of 6 independent variables was formed, which included 5
indicators of anthropogenic impact and radiation background for 24 administrative
units of Ukraine and Kyiv City (Fig. 2 ). Input variables were specific emissions (of
pollutants and carbon dioxide), specific discharges of reverse contaminated water,
specific waste and radiation background (Fig.3). The population mortality rate was
selected as level losses of the human capital due to emergency pollution (the output
variable Y).
The active rules and membership functions operating concerning the output varia-
ble population mortality rate based on compliance with fuzzy-logical rules in
Dnipropetrovsk region is presented in Fig.4.
It is important, that the active rules and membership functions operating concern-
ing the output variable population mortality rate of Dnipropetrovsk region based on
compliance with fuzzy-logical rules. The mathematical framework, which lays in the
mechanism of the conclusion [19], identifies features of all the other stages of expert
system.
Fig. 2. Data set for assessing losses of the human capital due to man-made pollution based on
fuzzy-logical result in proposed new approach
Fig. 3. The input variables of man-made pollution used on fuzzy-logical result
Fig. 4. The active rules and membership functions operating concerning the output variable
“mortality” based on compliance with fuzzy-logical rules in Dnipropetrovsk region
The developed rules and functions of membership also gave the appropriate result for
the initial variable mortality of the population of the remaining 23 regions and Kyiv.
The error of the obtained results does not exceed 0.05. This indicates the possibility of
using a new approach to estimating the mortality rate of the population of Ukraine
caused by pollution from the future in the future.
Simulated results of the population mortality rate as the level losses of the human
capital due to emergency pollution for all Ukrainian regions are presented in Table 1.
Table 1. The results of the population mortality rate simulated by the new proposed approach
used on fuzzy-logical result
Anthropogenic impact Simulated
Specific emissions Specific Specific waste
discharges Radiat Population
of car-
Administrative of pollu- of carbon of reverse ion mortality rate,
of pollu- bon
units tants, kg dioxide, contamina backgr case per 100
tants, kg dioxide,
per ton per ted water., ound thousand cash.
3 per person ton per
person person m per settlement
person
person
Vinnytsia region 112,29 4,06 0,61 1925,38 11,99 12 188
Volyn region 48,46 1,35 0,96 705,58 23,46 11 165
Dnipropetrovsk
354,65 11,40 115,79 88030,90 24,76 12,5 204
region
Donetsk region 391,90 14,44 127,53 12947,55 51,04 13,5 241
Zhytomyr region 67,54 1,26 2,36 683,11 58,24 14,5 189
Transcarpathian
57,48 0,88 1,59 447,94 6,62 12 148
region
Zaporizhzhia
177,01 7,84 39,77 3428,69 162,78 11 189
region
Ivano-Frankivsk
180,27 8,68 0,72 1290,20 5,43 12 188
region
Kyiv region 178,91 5,92 2,32 1751,29 24,04 13 216
Kirovograd
74,16 1,71 5,02 40284,57 10,95 12 245
region
Luhansk region 234,66 9,48 44,76 7403,59 470,42 12 209
Lviv region 99,93 2,16 17,32 1318,69 6,77 11 194
Mykolaiv region 74,22 2,56 22,16 2109,16 29,83 13 206
Odesa region 70,56 2,09 43,00 558,28 11,77 11 189
Poltava region 121,88 2,79 2,73 4292,27 233,75 11,5 244
Rivne region 52,21 1,82 6,92 1107,62 89,38 12,5 209
Sumy region 70,07 1,92 19,24 1064,29 119,23 12 187
Ternopil region 60,34 1,21 2,78 929,45 5,66 11,5 159
Kharkiv region 116,38 4,85 4,74 880,88 112,56 12 245
Kherson region 68,26 1,21 1,85 450,38 68,63 10 221
Khmelnytskyi
60,65 2,13 0,00 1119,56 11,42 11,5 166
region
Cherkasy region 115,38 3,47 3,15 1493,73 83,54 12,5 240
Chernivtsi re-
45,30 0,77 2,20 606,92 6,39 12 132
gion
Chernihiv region 86,84 2,32 17,63 687,14 90,09 13 247
Kyiv 91,11 3,44 7,38 471,85 27,70 12 200
5 Conclusions
The problem of estimating the loss of human capital due to man-made pollution
caused by man-made and natural disasters is considered in the article.
To develop a methodological approach for estimating the loss of human capital due
to man-made pollution caused by emergencies, the risks of possible losses from mor-
tality caused by environmental pollution due to emergencies were taken into account.
The world-famous interactive databases of emergencies have been studied and
compared, where not only the amount of damage caused by emergencies is collected,
but also the amount of losses from losses of human capital caused by emergencies.
The classical approaches used in the world to assess man-made losses from envi-
ronmental pollution (emissions, discharges, waste) do not cover the full range of di-
rect and indirect losses caused to human capital. The approach recommended by the
United Nations Environment Program (UNEP) is intended only to assess solid waste
flows, and in no way takes into account the potential damage from these contami-
nants.
The proposed concept of the approach assessing losses of the human capital due to
man-made pollution is based on the theory of fuzzy sets. A qualitatively new ap-
proach to the economic assessment of possible (predicted) losses of human capital
caused by pollution from emergencies is proposed. Pollution is considered as a set of
emissions into the atmosphere, discharges into water, pollution of land with liquid and
solid industrial waste, etc. This was taken into account when choosing a data set to
test the concept of the proposed approach. It takes into account the risks of possible
losses from mortality using fuzzy logic. The expediency of applying the theory of
fuzzy sets to calculate the mortality rate due to pollution from emergencies is substan-
tiated.
The suggested approach to assessing the loss of human capital can be used for var-
ious emergencies of man-made and natural nature, among the consequences of which
is environmental pollution in future periods.
References
1. Kamaletdinova, G.R., Onevsky, M.P., Skvortsov, S.A. Human safety in a man-made eco-
system. International Journal of Safety and Security Engineering, 8 (3), 406 – 412. DOI:
10.2495/SAFE-V8-N3-406-412. (2018).
2. Grebennik, I., Reshetnil, V., Ovezgeldyyev, A., Ivanov V., Urniaieva, I. Strategy of effec-
tive decision-makig in planning and elimination of consequences of emergency situations.
In: Information Technology in Disaster Risk Reduction (ITDRR 2018): Y. Murayama et al
(Eds)., IFIP AICT 550, pp.66-75, (2019). http://doi.org/10.1007/978-3-030-32169-7_6.
3. Korneev, A. M.; Salfetnikov, M. V.; Glazkova, Y. A.; Lavrukhina, T. V.; Abdullakh, L. S.
Emergency Risks Situations Computation for Intellectual Support in Administration of
Decisions. Journal of Computational and Theoretical Nanoscience, 16 (4), pp. 105-108.
(2019). DOI: https://doi.org/10.1166/jctn.2019.7706.
4. Bublyk, M., Matseliukh, Y., Motorniuk, U., Terebukh, M.: Intelligent system of passenger
transportation by autopiloted electric buses in Smart City. In: CEUR Workshop Proceed-
ings, 2604, 1280. (2020) http://ceur-ws.org/Vol-2604/paper81.pdf
5. Grytsyshen D. The economic and environmental consequences of emergency situations in
the system of accounting objects. Economic Annals-XXI, 7-8 (1), 113-118 (2015).
6. A. N. ValyaevS. V. KazakovA. A. ShamaevaO. V. StepanetsH. D. PassellV. P. Solodu-
khinV. A. PetrovG. M. AleksanyanD. I. AitmatovaR. F. MamedovM. S. Chkhartishvili
Assessment of Risks and Possible Ecological and Economic Damages from Large-Scale
Natural and Man-Induced Catastrophes in Ecology-Hazard Regions of Central Asia and
the Caucasus. In: Nuclear Risks in Central Asia. Part of the NATO Science for Peace and
Security Series Series C: Environmental Security book series (NAPSC) pp 133-149 (2008)
DOI https://doi.org/10.1007/978-1-4020-8317-4_13.
7. Australian Emergency Management Knowledge Hub (2020).
https://knowledge.aidr.org.au/disasters/.
8. Canadian Disaster Database (2020). https://www.publicsafety.gc.ca/cnt/rsrcs/cndn-dsstr-
dtbs/index-en.aspx.
9. EM-DAT: The International Disaster Database. (2020). http://www.emdat.be/index.html.
10. Munich Re’s NatCatSERVICE (2020). https://www.munichre.com/en/solutions/for-
industry-clients/natcatservice.html
11. Sigma explorer - catastrophe and insurance market data. Swiss Re Institute (2020).
https://www.sigma-explorer.com/.
12. Analytical Review of the State of Technogenic and Natural Safety in Ukraine for 2016,
The State Emergency Service of Ukraine (2017). http://www.dsns.gov.ua/ua/Analitichniy-
oglyad-stanu-tehnogennoyi-ta-prirodnoyibezpeki-v--Ukrayini-za-2015-rik.html, accessed
June 7, 2018.
13. Tymchenko, O., Kolodij, R. "Use of wireless technologies for the requirements of emer-
gency medical procedures", Modern Problems of Radio Engineering, Telecommunications
and Computer Science - Proceedings of the 10th International Conference, TCSET'2010,
Art No. 5446157, 2010, pp. 153-155.
14. Kovalchuk, A., Lotoshynska, N. "Assessment of damage to buildings in areas emergency
situations", Proceedings of the 2016 IEEE 1st International Conference on Data Stream
Mining and Processing, DSMP 2016, Art No. 7583528, pp. 152-156. (2016). DOI >
10.1109/DSMP.2016.7583528.
15. Ivanenko, O. (2020). Implementation Of Risk Assessment For Critical Infrastructure Pro-
tection With The Use Of Risk Matrix. ScienceRise, (2), 26-38.
https://doi.org/10.21303/2313-8416.2020.001340.
16. Lytvyn, V., Vysotska, V., Veres, O., Rishnyak, I., Rishnyak, H.: The Risk Management
Modelling in Multi Project Environment. In: Proceedings of the International Conference
on Computer Sciences and Information Technologies, CSIT, 32-35. (2017).
17. Chyrun, L., Vysotska, V., Kis, I., Chyrun, L. Content Analysis Method for Cut Formation
of Human Psychological State. In: Proceedings of the 2018 IEEE 2nd International Con-
ference on Data Stream Mining and Processing, DSMP 2018, 8478619, pp. 139-144
(2018).
18. Schurr, M., De Tuya, M., Noll, K. Risk–Informed Decision Making in Information System
Implementation Projects: Using Qualitative Assessment and Evaluation of Stakeholders’
Perceptions of Risk. In Proceedings of the 50th Hawaii International Conference on Sys-
tem Sciences (2017). http://hdl.handle.net/10125/41903.
19. Kuzmin, O., Bublyk, M.: Methodological foundations of economic evaluation of techno-
genic losses of national economy. In: Economics, Entrepreneurship, Management, 1, 1, pp.
1–8. (2014).
20. Papanikolaou, M., and Xenidis Y. Risk-Informed Performance Assessment of Construc-
tion Projects. Sustainability, 12(13), 5321 (2020). https://doi.org/10.3390/su12135321.
21. Methodology for assessing losses from the consequences of man-made and natural emer-
gencies: Resolution of the Cabinet of Ministers of Ukraine of February 15, 2012, N 175.
(2012). https://zakon.rada.gov.ua/laws/show/175-2012-n
22. Bodnar, I., Bublyk, M., Veres, O., Lozynska, O., Karpov, I., Burov, Y., Kravets, P.,
Peleshchak, I., Vovk, O., Maslak, O.: Forecasting the risk of cervical cancer in women in
the human capital development context using machine learning. In: CEUR Workshop Pro-
ceedings, 2631, 491-501. (2020). http://ceur-ws.org/Vol-2631/paper36.pdf.
23. Zimmerman, R.: Financing sustainable infrastructure: Reconciling disaster and traditional
financial resources. In.: International Conference on Sustainable Infrastructure 2017: Poli-
cy, Finance, and Education. Soibelman, L., and Peña-Mora, F. (Eds.) (2017).
https://doi.org/10.1061/9780784481202.017.
24. Soprun, O., Bublyk, M., Matseliukh, Y., Andrunyk, V., Chyrun, L., Dyyak, I., Yakovlev,
A., Emmerich, M., Osolinsky, O., Sachenko, A.: Forecasting temperatures of a
synchronous motor with permanent magnets using machine learning. In: CEUR Workshop
Proceedings, 2631, 95-120. (2020). http://ceur-ws.org/Vol-2631/paper8.pdf.
25. Zadeh L.A. Fuzzy Logic. In: Meyers R. (eds) Encyclopedia of Complexity and Systems
Science. Springer, New York, NY. (2009). https://doi.org/10.1007/978-0-387-30440-
3_234.