Modeling the Ukraine Black Sea Economic Region GRP Dependence on Socio-Economic Indicators
This paper presents the results of simulation socio-economic and ecological processes on example of the southern Ukraine under conditions of incomplete information. Construction and implementation of the information technology computer monitoring model of the complex processes state at various level...
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Міжнародний науково-навчальний центр інформаційних технологій і систем НАН та МОН України
2013
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irk-123456789-836622015-06-22T03:02:14Z Modeling the Ukraine Black Sea Economic Region GRP Dependence on Socio-Economic Indicators Bulgakova, O. Наукові статті This paper presents the results of simulation socio-economic and ecological processes on example of the southern Ukraine under conditions of incomplete information. Construction and implementation of the information technology computer monitoring model of the complex processes state at various levels Представлено результати моделювання соціально-економічних явищ та екологічних процесів системного характеру на прикладі півдня України в умовах неповноти інформації. Сконструйована та реалізована типова інформаційна технологія комп’ютерного моніторингу стану складних процесів різного рівня. Представлены результаты моделирования социально-экономических явлений и экологических процессов системного характера на примере юга Украины в условиях неполноты информации. Сконструированная и реализована типовая информационная технология компьютерного мониторинга состояния сложных процессов разного уровня. 2013 Article Modeling the Ukraine Black Sea Economic Region GRP Dependence on Socio-Economic Indicators / O. Bulgakova // Індуктивне моделювання складних систем: Зб. наук. пр. — К.: МННЦ ІТС НАН та МОН України, 2013. — Вип. 5. — С. 105-113. — Бібліогр.: 6 назв. — англ. XXXX-0044 http://dspace.nbuv.gov.ua/handle/123456789/83662 681.5.015 en Індуктивне моделювання складних систем Міжнародний науково-навчальний центр інформаційних технологій і систем НАН та МОН України |
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Наукові статті Наукові статті Bulgakova, O. Modeling the Ukraine Black Sea Economic Region GRP Dependence on Socio-Economic Indicators Індуктивне моделювання складних систем |
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This paper presents the results of simulation socio-economic and ecological processes on example of the southern Ukraine under conditions of incomplete information. Construction and implementation of the information technology computer monitoring model of the complex processes state at various levels |
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Article |
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Bulgakova, O. |
| author_facet |
Bulgakova, O. |
| author_sort |
Bulgakova, O. |
| title |
Modeling the Ukraine Black Sea Economic Region GRP Dependence on Socio-Economic Indicators |
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Modeling the Ukraine Black Sea Economic Region GRP Dependence on Socio-Economic Indicators |
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Modeling the Ukraine Black Sea Economic Region GRP Dependence on Socio-Economic Indicators |
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Modeling the Ukraine Black Sea Economic Region GRP Dependence on Socio-Economic Indicators |
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Modeling the Ukraine Black Sea Economic Region GRP Dependence on Socio-Economic Indicators |
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modeling the ukraine black sea economic region grp dependence on socio-economic indicators |
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Міжнародний науково-навчальний центр інформаційних технологій і систем НАН та МОН України |
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2013 |
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Наукові статті |
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http://dspace.nbuv.gov.ua/handle/123456789/83662 |
| citation_txt |
Modeling the Ukraine Black Sea Economic Region GRP Dependence on Socio-Economic Indicators / O. Bulgakova // Індуктивне моделювання складних систем: Зб. наук. пр. — К.: МННЦ ІТС НАН та МОН України, 2013. — Вип. 5. — С. 105-113. — Бібліогр.: 6 назв. — англ. |
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Індуктивне моделювання складних систем |
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2025-07-06T10:28:43Z |
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1836893051257094144 |
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Oleksandra Bulgakova
УДК 681.5.015
MODELING THE UKRAINE BLACK SEA ECONOMIC REGION GRP
DEPENDENCE ON SOCIO-ECONOMIC INDICATORS
Oleksandra Bulgakova
Mykolaiv V.O.Suhomlynsky National University, Nikolska str., 24, Mykolaiv, 54030, Ukraine
sashabulgakova@list.ru
Представлено результати моделювання соціально-економічних явищ та екологічних
процесів системного характеру на прикладі півдня України в умовах неповноти інформації.
Сконструйована та реалізована типова інформаційна технологія комп’ютерного моніторингу
стану складних процесів різного рівня.
Ключові слова: індуктивне моделювання, ітераційні алгоритми, метод групового
урахування аргументів, МГУА, узагальнений ітераційний алгоритм, інформаційні технології,
ВРП, соціально-економічні явища та процеси.
This paper presents the results of simulation socio-economic and ecological processes on
example of the southern Ukraine under conditions of incomplete information. Construction and
implementation of the information technology computer monitoring model of the complex
processes state at various levels
Keywords: Inductive modeling, iterative algorithm, GMDH, generalized iterative algorithm,
information technologies, GRP, socio-economic processes.
Представлены результаты моделирования социально-экономических явлений и
экологических процессов системного характера на примере юга Украины в условиях
неполноты информации. Сконструированная и реализована типовая информационная
технология компьютерного мониторинга состояния сложных процессов разного уровня.
Ключевые слова: индуктивное моделирование, итерационные алгоритмы, метод
группового учета аргументов, МГУА, обобщенный итерационный алгоритм,
информационные технологии, ВРП, социально-экономические явления и процессы.
Introduction
Each region of Ukraine have its own peculiarities and differences in socio-
economic development that are associated with a spatial basis, presence of minerals,
availability human resources and so on. State regional policy aims to provide the
necessary balance between individual regions by encouraging the most advanced
straightening and implementation mechanisms to overcome economic and social
problems of weak regions. This policy should encourage the integration processes in
society, to resist risk increasing regional disparities.
Creating favorable conditions for sustainable and uniform regional
development is one of the most important tasks of regional development, which
includes: structural and technological changes in the economy, the development of
small towns and reduce inter-regional gap in socio-economic development.
At the regional level summary measure that reflects the level of economic
development of the region is the gross regional product, i.e. GRP, is one of several
Індуктивне моделювання складних систем, випуск 5, 2013 105
Modeling the Ukraine black sea economic region GRP dependence
measures of the size of its economy. GRP is defined as the market value of all final
goods and services produced area in a given period of time.
Paper presents results of simulation Ukraine's Black Sea economic region
GRP, which occupies the territory of Odessa, Nikolaev, Kherson regions and the
Autonomous Republic Crimea, located in the southern and south-western parts of
Ukraine, dependence on socio-economic indicators using generalized iterative
algorithm GMDH. According to the simulation results will be determined
informative parameters of socio-economic development, which affect the dynamics
of the region's GRP.
1. The generalized iterative algorithm
Let us briefly consider the iterative structure of algorithm used for solving the
general problem of search for a better model under such formulation:
)),ˆ,(,(minarg*
ff
XfyCRf θ
Φ∈
= (1)
where is an estimation of parameters for any partial model f∈Φ , CR is a model
quality criterion for selection of optimal model.
fθ̂
The set Φ of models being compared can be formed by various generators of
model structures of diverse complexities. All structure generators developed within
the GMDH framework naturally divided into two main groups – sorting-out and
iterative ones which differ by techniques of variants generation and organization of
search of a given criterion minimum. For simulation will be used the generalized
iterative algorithm, GIA GMDH, fig.1 [1].
Formally, in the general case for layer r define the GIA GMDH as follows:
1) the input matrix is ),,,,...,( 111 m
r
F
r
r xxyyX K=+ ,
2) apply the operators:
FjiClyyfy F
r
j
r
i
r
l ,1,,,,2,1),,( 21 ===+ K (2)
and
mjFiFmlxyfy j
r
i
r
l ,1,,1,,,2,1),,(1 ====+ K (3)
with a quadratic partial description
.),(
;),(
;),(
2
5
2
43210
3210
210
vauauvavauaavufz
uvavauaavufz
vauaavufz
+++++==
+++==
++==
(4)
3) for each description is the optimal structure (an example for the linear partial
description):
vdaudadavuf 322110),( ++= , (5)
Індуктивне моделювання складних систем, випуск 5, 2013 106
Oleksandra Bulgakova
where , are structural elements of the binary vector
taking values 1 or 0 (inclusion or not a relevant argument). Then the
best model will describe: , where
3,2,1, =kdk }1,0{=kd
)( 321 dddd =
),,( optdvuf
12,minarg
,1
−==
=
p
l
ql
opt qCRd , (6) ),,(),( optopt dvufvuf =
4) the algorithm stops when the condition is checked, where
are criterion values for the best models of (r–1)-th and r-th layers respectively. If the
condition holds, then stop, otherwise jump to the next layer.
1−> rr CRCR 1, −rr CRCR
Fig.1. The generalized iterative algorithm schema
Define the GIA GMDH as many iterative and iterative combinatorial
algorithms, described by vector of three elements DM (Dialogue Mode), ІC
(Iterative-Combinatorial), MR (Multilayered-Relaxative), ie any iterative algorithm is
defined as a special case of a generalized: GIA (DM, IC, MR). This is possible with
the help of specialized program complex of modeling based on iterative algorithms
group method of data handling, which implemented the following features: automatic
and interactive options for organization of user interface, management through the
web interface, ensuring multiaccess. Constructed best model are presented by system
for the graphic and semantic analysis, determined the effect of the arguments on the
target factor, as well as analyzes and selects the most informative arguments [2].
Generalized specialized program complex schema is present on fig.2.
Індуктивне моделювання складних систем, випуск 5, 2013 107
Modeling the Ukraine black sea economic region GRP dependence
Fig.2. Generalized specialized program complex schema
2. Results of the economical process modeling
To identify the dependence of GRP growth (mln.) on the volume of socio-
economic indicators were taken into account
Industry:
х1 – Indices of industrial production, in% to previous year;
Production of major animal products:
х2 – Meat production in all categories of farms;
х3 – Milk production;
х4 – wool production;
х5 – eggs production;
Production of main agricultural crops:
х6 – grains and legumes;
х7 – sunflower seeds;
х8 – potatoes;
х9 – vegetables;
х10 – fruit and berries;
Fishing industry
х11 – fishing extraction and other aquatic resources;
Investment and construction activities:
х12 – commissioning housing;
Transport:
Індуктивне моделювання складних систем, випуск 5, 2013 108
Oleksandra Bulgakova
х13 – departure (transportation) of cargo;
х14 – departure (transportation) of passengers;
Foreign trade:
х15 – export goods and services;
х16 – import goods and services;
Internal trade:
х17 – retail turnover;
Job Market:
х18 – unemployment Rate;
х19 – the average monthly salary;
х20 – arrears of wages;
Data were taken on the State Statistics Committee [3-6].
Statistical sample of Ukraine's Black Sea economic region (fig.3) for the period
from 2004 (1 quarter) to 2011 (4 quarter) contains totally 20 variables and 32 points
and is divided into three parts: training А (20 points), testing В (8 points}, and
examination С (4 points, 2011 year) sub-samples.
Fig.3. Ukraine's Black Sea economic region
Tab.1 and fig.4-7 shows result of modeling GRP values.
Індуктивне моделювання складних систем, випуск 5, 2013 109
Modeling the Ukraine black sea economic region GRP dependence
Table 1.
Result of modeling GRP values
Region AR
Model
accura
cy
Model
AR
Crimea 7,261 99
201918
1715141211
10862
000054,0693,5000056,0
87,1000354,0939,230048,0000017,0
0489,0067,00618,00025,079,8000
ххх
ххххх
ххххy
++−
−++−+−
−++−−=
Mykolaiv
region 0,787 99
2
17
10
2
1219171613
121173
10*24
022,0284,900026,0000027,000023,0
815,100549,001315,00306,010,1697
х
ххххх
ххххy
−+
+++++−
−+−−+=
Kherson
region 202,01 87
2
71964 000037,0228,8063,01376,159,538 ххххy ++++−=
Odessa
region 189.23 90 2
101915915
191597
000606,00093,000014,0
868,3583,17541,000407,099,18548
ххххх
ххххy
−−−
−+++−−=
The accuracy of obtained models was calculated using the formula for
coefficient of determination:
( )
( )
%,100
ˆ
1
2
1
2
2
∑
∑
=
=
−
−
= n
i
i
n
i
i
yy
yy
R (7)
where y is the average value, is the model output. iŷ
As the obtained dependence shows
- for AR Crimea among all the 20 investment indicators only 12 of them have
the most significant effect on GRP: meat production in all categories of farms; grains
and legumes; potatoes; fruit and berries; fishing extraction and other aquatic
resources; commissioning housing; departure (transportation) of passengers; export
goods and services; retail turnover; unemployment Rate; the average monthly salary;
arrears of wages;
- for Mykolaiv region among all the 20 investment indicators only 8 of them
have the most significant effect on GDP: milk production; sunflower seeds; fishing
extraction and other aquatic resources; commissioning housing; departure
(transportation) of cargo; import goods and services; retail turnover; the average
monthly salary;
Індуктивне моделювання складних систем, випуск 5, 2013 110
Oleksandra Bulgakova
- for Kherson region among all the 20 investment indicators only 4 of them
have the most significant effect on GDP: wool production; grains and legumes; the
average monthly salary; sunflower seeds;
- for Odessa region among all the 20 investment indicators only 5 of them have
the most significant effect on GDP: sunflower seeds; vegetables; export goods and
services; the average monthly salary; fruit and berries;
The Autonomous Republic Crimea
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
Years
G
R
P
GRP (forecast)
GRP (real)
GRP (forecast) 9903 12736 16238 20790 27366 27395 32425 38222
GRP (real) 9901 12848 16044 20874 27365 27396 32426 38220
2004 2005 2006 2007 2008 2009 2010 2011
Fig.4. Result of modeling GRP AR Crimea values, on years
Mykolaiv region
0
5000
10000
15000
20000
25000
30000
Years
G
R
P
GRP (forecast)
GRP (real)
GRP (forecast) 7924 9565 11866 14756 19389 20319 24031 27627
GRP (real) 7934 9553 11876 14767 19410 20336 24055 27663
2004 2005 2006 2007 2008 2009 2010 2011
Fig.5. Result of modeling GRP Mykolaiv region values, on years
Індуктивне моделювання складних систем, випуск 5, 2013 111
Modeling the Ukraine black sea economic region GRP dependence
Kherson region
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
20000
Years
G
R
P
GRP (forecast)
GRP (real)
GRP (forecast) 4847 6183 7745 8749 13176 13433 15671 18446
GRP (real) 5200 5713 7565 9034 13174 13436 15649 18448
2004 2005 2006 2007 2008 2009 2010 2011
Fig.6. Result of modeling GRP Kherson region values, on years
Odessa region
0
10000
20000
30000
40000
50000
60000
70000
Years
G
R
P
GRP (forecast)
GRP (real)
GRP (forecast) 16601 21177 24926 33253 46852 48639 53869 61509
GRP (real) 17029 20762 24898 33116 46994 48647 53878 61499
2004 2005 2006 2007 2008 2009 2010 2011
Fig.7. Result of modeling GRP Odessa region values, on years
Індуктивне моделювання складних систем, випуск 5, 2013 112
Oleksandra Bulgakova
Results of experiments show that features industry growth in industrial
production resulted in its regional differentiation. Thus, the priority sector of the
Black Sea Economic Region is the food industry and processing of agricultural
products. These conclusions may serve as a basis for further study of the effect of
uneven level and pace of economic development of the region for non-linear nature
of the economy of Ukraine in general
3. Conclusion
Was solved task of simulation Ukraine's Black Sea economic region GRP
dependence on socio-economic indicators using generalized iterative algorithm
GMDH and program complex. According to the simulation results were determined
informative parameters of socio-economic development, which affect the dynamics
of the region's GRP. For solve this task was used the generalized iterative GMDH
algorithm based on hybridization of iterative and combinatorial schemes and use of
on-line technologies is newly developed for building of complex system models in
the polynomial class.
References
1. Stepashko V.S., Bulgakova O.S. Generalized iterative algorithm of the group
method of data handling // USiM. – 2013. – № 2. – P: 5-18.
2. Bulgakova O.S., Zosimiv V.V., Stepashko V.S. Program complex modeling of
complex systems based on iterative algorithms with the ability of GMDH network
access: 14-th International conference SAIT 2012, Kyiv, Ukraine, 176-178 p.
3. The State Statistics Committee of Mykolaiv region: mk.ukrstat.gov.ua
4. The State Statistics Committee of Odessa region: od.ukrstat.gov.ua
5. The State Statistics Committee of Kherson region: ks.ukrstat.gov.ua
6. The State Statistics Committee of AR Crimea region: sf.ukrstat.gov.ua
Індуктивне моделювання складних систем, випуск 5, 2013 113
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