=Paper=
{{Paper
|id=Vol-3126/paper54
|storemode=property
|title=Development of a spatial decision-making support system for the location of technogenic hazard objects
|pdfUrl=https://ceur-ws.org/Vol-3126/paper54.pdf
|volume=Vol-3126
|authors=Svitlana Kuznichenko,Iryna Buchynska
}}
==Development of a spatial decision-making support system for the location of technogenic hazard objects==
https://ceur-ws.org/Vol-3126/paper54.pdf
Development of a Spatial Decision-Making Support System for
yhe Location of Technogenic Hazard Objects
Svitlana Kuznichenko1, Iryna Buchynska2
1, 2
Odessa State Environmental University, 15 Lvivska Str, Odesa, 65016, Ukraine
Abstract
The paper proposes an approach to the development of a spatial decision-making support
system for the location of technogenic hazard objects. To solve the problem of ranking the
territory according to the degree of suitability for placing hazard objects, methods of multiple-
criteria decision-making and fuzzy models of spatial data processing are used. The use of the
apparatus of fuzzy logic allows taking into account expert knowledge and judgments, partially
compensates for the uncertainty of the initial information. During building the database, the
concept of fuzzy relational databases was used, which allows you to extend the relational
model to represent fuzzy data. This approach allows using relational structures to store the
judgments of experts using the apparatus of fuzzy sets in GIS.
Keywords
Geographic information system, multiple-criteria decision analysis, fuzzy sets, site selection
analysis.
opportunities to include the preferences of a
1. Introduction decision-maker (DM). In addition, the
complexity of spatial relations in some problems
cannot be represented cartographically.
Modern geoinformation systems (GIS) are an Therefore, for the last 20 years, GISs have been
essential component of decision support systems
actively integrating multiple-criteria decision
(DSS) due to the advanced functions of storage,
analysis (MCDA) methods [2-4] which expand
processing and analysis of geodata, modeling the capabilities of GISs.
tools, and the availability of visualization tools. Methods of multiple-criteria decision analysis
Spatial problems, in particular the problem of (MCDA) allow to structurize the problem of
determining the suitability of sites for
decision-making in the geographical sphere, take
construction objects, are by their nature always into account value judgments (i.e., preferences
multiple-criteria [1]; therefore spatial DSSs are for criteria and/or alternative solutions), provide
often used in cases when a large number of transparency of decision-making for a DM, and
alternatives must be assessed on the basis of the ability to take into account both qualitative
several criteria..1 and quantitative criteria evaluation of all
GIS capabilities to generate a set of alternative solutions.
alternatives and select the best solution are It should be noted that the major part of
usually based on surface analysis, proximity modern general-purpose GISs does not contain
analysis, and overlay analysis. Overlay built-in full-featured tools that can fulfill a
operations allow us to identify alternatives that complex MCDA procedure. The use of separate
simultaneously meet a set of criteria according to software and tools and the lack of a single
the decision rule, but they have limited system for processing expert knowledge
ISIT 2021: II International Scientific and Practical Conference
increases the duration of pre-project work, i.e.,
«Intellectual Systems and Information Technologies», September increases the life cycle of decision-making and
13–19, 2021, Odesa, Ukraine consequently increases the probability of
EMAIL: skuznichenko@gmail.com (A. 1);
buchiskayira@gmail.com (A. 2);
erroneous results at different stages. One of the
ORCID: 0000-0001-7982-1298 (A. 1); possible ways to overcome the above-mentioned
0000-0002-0393-2781 (A. 2) problems is the development and integration of
©️ 2021 Copyright for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
software that implements the MCDA procedures
CEUR Workshop Proceedings (CEUR-WS.org)
into GISs.
Individual attempts to fully integrate MCDA the instrument of fuzzy set theory and fuzzy
and GIS tools within the common interface have logic;
identified problems due to the lack of flexibility visualization of modeling results for
and interactivity of such systems, which cannot different decision making strategies in the
provide the needed freedom of action for form of a comprehensive suitability map.
analysts [5]. Therefore, the choice of procedure
and appropriate methods of MCDA, which can
2. The main research material
provide a better solution to a particular problem,
is an urgent task for developers. 2.1. Multiple-criteria model of
Analysis of recent research and publications technogenic hazard objects location
shows that the combination of MCDA and GIS is
a fundamental tool for solving spatial problems
based on fuzzy logic
in many areas [6-9]. Over the last few decades,
significant progress has been made in the Let us formulate the problem to determine the
development of methods for the multiple-criteria degree of suitability of the territory for the
analysis of the suitability of territories [10-12] location of man-made hazardous objects on it
and the choice of locations for spatial objects [15]:
[13-15]. A,C,F,P;D , (1)
The peculiarity of the multiple-criteria
decision analysis on the location of man-made where A = {a1, a2,…, am} is a finite set of
hazardous and industrial objects is the need to alternatives; C = {C1, C2,…, Cn} – a set of
take into account the ecological status and criteria by which alternatives are assessed; F –
prospects of the socio-economic development of criteria-based assessment procedure; P – a
the region, the impact of this object on the system of the DM preferences, contains
environment and anthropogenic environment, as information on the alternatives assessments for
well as the current environmental legislation and each criterion; D – the decisive rule, specifies the
sanitation. Preliminary examinations, in procedure for performing the desired action on a
particular, ecological examinations at the site of set of alternatives (selection, ranking, sorting of
the planned location of the object, are a alternatives).
mandatory condition. This justifies the need to In the geographical context, the MCDA
take into account expert knowledge and use process includes a set of geographically defined
methods based on expert assessments. alternatives (e.g., land plots) and a set of
In addition, we have to often encounter assessment criteria presented as map layers. The
inaccuracies in the source spatial information and analysis is to combine the criteria attributes
the need to use criteria that cannot be formalized, according to the DM preferences using the
as well as uncertainty among experts as to the decision rule (combining rule).
relative importance of the criteria and the It is assumed that the criteria layers are
acceptable decision strategy, i.e., compromise represented in a raster data model that has the
between the alternatives assessments according form of a two-dimensional discrete rectangular
to different criteria. To take into account such grid x×y. Each raster cell is an alternative that is
uncertainties, an approach based on the use of described by its spatial data (geographical
"soft" computing and fuzzy set theory in MCDA coordinates) and attribute data (criteria values).
methods is considered suitable [16]. Thus, in the Let us write a set of alternatives A assessed by
information system based on the processing of the criteria Cj:
geospatial information, in order to support
decision making on the location of spatial
А a |i 1 m,j 1 n ,
ij (2)
objects, the following tasks must be solved: where aij – the value of the alternative attribute,
automated processing of the source i.e., the value of the attribute according to the j-th
heterogeneous geospatial information; criterion and the i-th alternative; n – a number of
ranking of territories according to the criteria; m = mx•my – the number of alternatives
degree of suitability for placement of (raster cells).
objects on the basis of a combination of The MD preferences for the criteria
processing of the geospatial information assessment are determined by assigning the
with estimates and judgments of experts criteria weights wj, where j = 1, 2, ..., n.
with the help of the MCDA methods using
A complete multiple-criteria mathematical triangular and trapezoidal (piecewise
model of the location of man-made hazardous linear);
objects based on the fuzzy logic is given in [17]. nonlinear (Gaussian function, sigmoidal
The model is adapted to the location of landfills function, spline);
for solid domestic waste (SDW). Landfills are LR-representation of membership
designed in accordance with state construction functions.
standards, which are given in Table 1.
It should be noted that the designed model
allows us to enter an unlimited number of
criteria, such as the prevailing wind direction,
surface slope, etc
Table 1
Requirements for the construction of landfills
SDW according to DBN V.2.4-2
Criterion Thresholds
Distance from airports and 15 km
airfields
Distance from the edge of 3000 m
open reservoirs, reserves,
seacoast
Distance from bridge border 1000m
Distance from residential and 500 m
public buildings
Distance from agricultural 200 m
land, road and railways
Distance from the border of 50 m Figure 1: Types of membership functions: a)
the forest and forest plant triangular; b) trapezoidal; c) U-shaped; d) Z-
Depth of soil water at least 2 m shaped; e) S-shaped
One of the important stages of the MCDA is Trapezoidal MF in the general case can be
criteria standardization – the transformation of given analytically by the expression:
criteria attributes into comparative units, usually 0, x a
in a range of [0,1]. In [17], a procedure for the
criteria fuzzification, i.e., transformation into a (x a)/(b a),a x b
1,b x c
fuzzy set, is proposed for this purpose based on
fТ x;a,b,c,d (d x)/(d c),c x d
(4)
an expert assessment of the fuzzy membership
function.
0,d x
Thus, the description of spatial information
based on the instrument of fuzzy set theory is
based on the transformation of the attribute
values of the k-th layer into the value of the where a, b, c, d – some numerical parameters that
membership degree of the fuzzy set Ṽk: take arbitrary real values and are ordered by the
relation: a b c d.
Vk (a, v k (a))|a U , v k (a):a [0,1], (3) The use of these functions reduces the
where a – the value of the attribute, U – a numerical calculations and, correspondingly, the
continuous set of attribute values. computational resources required to store
As a rule, the membership function is built individual values of the membership function.
with the participation of an expert (group of Criteria fuzzification allows for the further
experts) so that the membership degree is combining of the criteria using fuzzy derivation
approximately equal to the intensity of the rules. Fuzzy arithmetic intersection or combining
manifestation of some factor. In practice, the operations can be used, which in this case can be
following types of membership functions are considered as non-compensatory aggregation
used (Fig. 1): methods.
Thus, the use of fuzzy set theory to
standardize the instrument criteria layers allows
to take into account the uncertainty of the source according to ASTER space images with a raster
information and the experience and judgment of cell size of 27 m. Depending on the specifics of
experts, as well as to obtain a more informative the tasks, additional specialized layers can be
map of suitability by determining the suitability used (especially protected areas, fisheries, etc.).
of alternatives: from 0 – "unsuitable," to 1 – Individual workflows have been designed as
"absolutely suitable". The higher the suitability in-house tools using the ModelBuilder visual
rank of the alternative, the more suitable the constructor and Python programming scripts.
alternative is for the object location. To provide the GIS application with the
necessary features and business logic, the
2.2. Designing of the structure of ArcObjects SDK extension for .NET was used,
with the help of which additional modules (add-
spatial DSS for the location of ons) that perform fuzzy spatial data processing
hazardous objects models, methods and algorithms of the MCDA
procedure were developed based on C# and
The decision support system (DSS) for the Windows Forms technology.
location of spatial objects was implemented as a
GIS application based on the ArcGIS for 2.3. Development of a fuzzy
Desktop platform by ESRI, which can be
published on the Internet as a web service for use
database model
by an unlimited number of desktop and mobile
clients using ArcGIS for Server server software. The concept of fuzzy relational databases was
The DSS structure is shown in Fig.2. The used in the building of the DSS database [18],
information needed to ensure the functioning of which allows to expand the relational model for
the system is stored in separate databases: the presentation of fuzzy data. This approach
cartographic – in a specialized geodatabase allows storing expert judgments with the help of
(GDB), expert information needed to process relational structures, using the instrument of
spatial data with the MCDA – in a database (DB) fuzzy sets as a basis for managing certain types
managed by the Microsoft SQL Server DBMS. of uncertainty in GIS.
Fuzzy data is represented by membership
functions, which can usually be determined by
several numerical parameters (Fig. 1). By storing
these parameters so that the requirements of
adequacy and integrity are met, one can manage
fuzzy data in a relational database. To do this, a
fuzzy metamodel is proposed, which manages
fuzzy data and connects with relational tables of
real objects (Fig. 3).
The is_fuzzy table indicates which attributes
and in which database tables are fuzzy. The
fuzzy_link table connects the MF type with an
attribute in a relational model of real objects. The
fuzzy_type table defines the type of MF:
Figure 2: The structure of the spatial DSS for the triangular, trapezoidal, Z-shaped, S-shaped.
location of technogenic hazard objects For the criteria attributes fuzzification, the
system involves linear MF, each of which is
The geodatabase of the system consists of presented by the numerical parameters in a
vector layers at a scale of 1:100000. Vector maps separate table. For example, the trapezoidal table
of land use, water bodies, settlements, railways, has the following attributes (fuzzy_id, a, b, c, d)
and highways are obtained by importing the to control the storing of trapezoidal fuzzy data.
Open Street Map database. Maps of agricultural The triangular table has the (fuzzy_id, a, b, c)
lands, reserves, housing, forests, and attributes correspondingly.
afforestation were obtained by using SQL The connection of the database fuzzy
queries to the land use map attribute table. metamodel with the geodatabase is shown in Fig.
Digital terrain model (DTM), as well as the 4. The survey_area table contains information
derived slope and exposure maps, were built
about the thematic raster layers of the studied
area that need fuzzification.
Figure 3: Fuzzy metamodel of a relational database
Figure 4: Informational model of fuzzy information storing in a relational database system
Figure 5: The structure of the spatial DSS for the location of technogenic hazardous objects
.
Using the is_fuzzy and fuzzy_link tables, the use of expert experience, as well as to obtain
each raster of the GDB gets an assigned certain a more informative map of the suitability of
type of MF. A relational example of a fuzzy territories by determining the suitability of
relational database is shown in Fig. 5. alternatives.
From the tables shown in Fig. 5, one can A metamodel of building a spatial decision
recover all fuzzy as well as clear data. For support system for the location of hazardous
example, the raster layer of distances from the objects, which extends the relational model for
transport network in the geodatabase of the the presentation of fuzzy data, is proposed. The
system is named Road. For the fuzzification of metamodel allows using relational structures to
its attributes, the trapezoidal MF will be used store attributive information, membership
with numerical parameters a = 200 m, b = 500 m, functions and expert judgments, using the
c = 1000 m, d = 5000 m, i.e., the greatest degree instrument of fuzzy sets as a basis for managing
of suitability according to this criterion will have certain types of uncertainty in GIS. The
alternatives located at a distance of 500 to 1000 relational approach to the organization of fuzzy
m from railways and highways. Based on the database makes it possible to use it as part of an
available numerical parameters of the trapezoidal organized storage structure, as well as to ensure
MF according to (4), the corresponding fuzzy the interaction of spatial and attributive data and
values can be obtained for the entire range of fuzzy database based on the use of queries
clear values of the criteria attributes, and a table received in the system, which greatly facilitates
is formed for reclassification of the raster by the system implementation and ensures integrity and
Reclassify geoprocessing ArcToolbox tool. consistency of all accumulated information about
hazardous objects to be located.
3. Conclusions
4. References
The paper presents a multiple-criteria
decision analysis model, and the structure of the [1] Chakhar S., Mousseau V. Spatial
spatial decision support system for the location multicriteria decision making // Shehkar S.
of hazardous objects in the form of a GIS and H. Xiong (Eds.), Encyclopedia of GIS,
application is developed. The use of fuzzy logic Springer-Verlag, New York, 2008. P. 747–
allows one to take into account expert knowledge 753.
and judgments, which partially compensates for [2] Chakhar S., Martel J.M. Enhancing
the uncertainty of the source information through geographical information systems
capabilities with multicriteria evaluation landfill siting considering land scarcity for
functions, Journal of Geographic waste disposal. // Waste Management. 2014.
Information and Decision Analysis, 2003. No. 34. Р. 2225 – 2238.
Vol. 7, No. 2. P. 69–71. [14] Rikalovic A., Cosic I., Lazarevic D. GIS
[3] Malczewski J (2006) GIS-based Based Multi-Criteria Analysis for Industrial
multicriteria decision analysis: a survey of Site Selection, Procedia Engineering, 2014.
the literature. International Geographical Vol. 69, No. 12. Р. 1054 – 1063.
Information Science 20(7):703–726. [15] Kuznichenko, S., Buchynska, I., Kovalenko,
[4] Malczewski J., Rinner C., Multicriteria L., Gunchenko, Y. Suitable site selection
Decision Analysis in Geographic using two-stage GIS-based fuzzy multi-
Information Science, 2015, Springer, New criteria decision analysis. Advances in
York. Intelligent Systems and
[5] Lidouh K. On themotivation behind MCDA Computing, 2020, 1080 AISC, стр. 214–
and GIS integration, Int. J. Multicriteria 230
Decision Making, 2013. Vol. 3, No. 2/3. P. [16] Zadeh L. A. Fuzzy sets. Information and
101–113. Control, 1965. Vol. 8 (3). P. 338–353.
[6] Afshari Ali, Vatanparast Mahdi, Ćoćkalo [17] Kuznichenko, S., Kovalenko, L.,
Dragan. Application of multi criteria Buchynska, I., Gunchenko, Y. ,
decision making to urban planning – A Development of a multi-criteria model for
review, Journal of Engineering Management making decisions on the location of solid
and Competitiveness (JEMC), 2016. Vol. waste landfills. Eastern-European Journal of
6/03. P. 46-53. Enterprise Technologies, 2018. Vol.2, No.
[7] Mardani A., Jusoh A., MD Nor K., Khalifah 3(92). P. 21–31. DOI: 10.15587/1729-
Z., Zakwan N.,Valipour A. Multiple criteria 4061.2018.129287
decision-making techniques and their [18] Petry FE (1996) Fuzzy Databases:
applications – a review of the literature from Principles and Applications. Kluwer
2000 to 2014, Economic Research, 2015. Academic Publishers Norwell, Boston, MA.
Vol. 28, No. 1. P. 516-571. USA, p 240.
[8] Kuznichenko, S., Buchynska, I., Kovalenko,
L., Tereshchenko, T. Integrated information
system for regional flood monitoring using
internet of things. CEUR Workshop
Proceedings, 2019, 2683, стр. 1–5
[9] M. Karpinski, S. Kuznichenko, N.
Kazakova, O. Fraze-Frazenko, D.
Jancarczyk.. Geospatial Assessment of the
Territorial Road Network by Fractal
Method. Future Internet. 12. 201 (2020).
10.3390/fi12110201.
[10] Lashari Z., Yousif M., Sahito N., Brohi S.,
Meghwar S., Khokhar U. D., Land Q.
Suitability Analysis for Public Parks using
the GIS Application, Sindh University
Research Journal (Science Series), 2017.
Vol.49(09). P. 505–512.
[11] Joerin F., Theriault M., Musy A. Using GIS
and outranking multicriteria analysis for
land-use suitability assessment, Int. j. of
geographical information science, 2001.
Vol. 15, No. 2. P. 153-174.
[12] Malczewski J (2004) GIS-based land-use
suitability analysis: a critical overview.
Progress in Planning, 62:3–6.
[13] Giovanni De Feo, Sabino De Gisi. Using
MCDA and GIS for hazardous waste