The paper considers the creation of intelligent information technology (IT) for multi-criteria vulnerability assessment of gas stations (GSs) to the main types of accidents. The proposed IT consists of 10 consecutive stages. The implementation of the first stages was related to the definition of the main types of accidents for GSs and consideration of GS vulnerability from the point of view of possible adverse consequences, for which 41 criteria were created by experts. It is proposed to apply the analytical hierarchy process for the analysis of expert data in order to obtain a secondary space of vulnerability criteria, without considering the lost lives. On the basis of the secondary space of criteria, data on the GS network was collected and a scheme for their preprocessing and coding was proposed. The GS vulnerability assessment was carried out on the basis of the developed generalized model. To interpret the results of GS vulnerability assessment, the use of fuzzy sets in the form of trapezoidal membership functions is proposed. Intelligent IT for GS vulnerability assessment is implemented in the form of a decision support system (DSS). The decision-maker, based on the entered data according to the vulnerability criteria of a certain GS, will receive a preliminary value of the GS vulnerability and an explanation of how it was obtained. A component of the DSS is a formed knowledge base on preevaluated GS with the possibility of editing and expanding it. The use of the proposed intelligent IT of multicriteria GS vulnerability assessment and DSS based on it provides an opportunity to make informed decisions on the assessment of potentially vulnerable facilities and risk management.
Ensuring the environmental safety of territories from possible accidents at potentially hazardous
facilities (such as large enterprises or warehouses storing hazardous substances, main pipelines,
pressure vessels etc.) is one of the state's socially important functions. This process becomes especially
important when there are hostilities in the country and such facilities can become a target, causing a
man-made disaster comparable to a small earthquake or tsunami (as an example, the Kakhovka Dam
destruction in Ukraine in 2023). During the 1st stage of the joint Ukrainian-British research project
between the National University of Odesa Polytechnic and the University of Portsmouth (UK) UUT14
Consequent Decision Optimizati within the framework of the UK-Ukraine Twinning initiative [
A primary assessment of the GSs vulnerability using IT is an integral part of risk assessment and management, which allows government agencies to make informed decisions and take preventive measures to reduce the scale of the negative impact of a possible accident, thereby improving the social and economic well-being of the country, as well as the environmental safety of the territories.
The AHP is one of the most popular methods for organising and analysing complex decision-making.
It was developed in the 1970s by Thomas Saaty [
FL methods for evaluating GSs from various aspects are used in modern researches. In [
The combined use of multi-criteria decision analysis (MCDA) and GIS is also used for a
comprehensive assessment of GSs, in particular in the following researches. The integrated use of
environmental impact assessment and AHP with the subsequent visualisation of data in GIS to
determine the suitability of a land plot for a GS is discussed in [
The aim of the research, which is considered in this paper, is to develop an intelligent IT for multicriteria assessment of the GSs vulnerability to the occurrence of the main types of accidents, the implementation of which will allow for a more rapid preliminary examination of the technogenic safety of GSs.
The main objectives to be performed to achieve the aim: • • •
Development of the main steps of intelligent IT for multi-criteria assessment of the GSs
vulnerability in relation to the main types of accidents, based on a previous study [
The following methods are used in the study: a MCDA method for assessing the vulnerability of GSs (AHP), a method of intellectual processing and coding of input data according to the criteria of GS vulnerability, a FL method for categorical assessment of GS vulnerability, a gradient scale and coordinate normalisation method for visualising.
The development of an intelligent IT that will allow to determine the vulnerability of GS to the main
types of accidents discussed in [
Identification of the main types of accidents (expert (E)); Development of the primary space of economic, environmental and social criteria (EESC) for assessing the vulnerability of GSs to the main types of accidents (E, data scientist (DS)); Obtaining a vector of weights of EESC based on AHP (DS); Development of a secondary space of EESC for assessing the GSs vulnerability (DS); Obtaining estimates of the values of EESC of the secondary space for each GS based on public data (E, DS); • • • • •
Preliminary processing and coding of the obtained public data in the secondary space (DS); Obtaining a vector of weights of EESC based on AHP in the secondary space (DS); Improving the general model for assessing the GSs vulnerability, considering stages 1-7 (DS); Determination and interpretation of the GSs vulnerability assessment using FL (DS); Building a knowledge base for assessing the GSs vulnerability to main types of accidents (DS).
Considering the above stages, Fig. 1 shows the developed intelligent IT assessment of the GSs vulnerability to the main types of accidents in the form of an DSS.
At the 1st stage of the above-described list, during the development of intelligent IT for assessing the
GSs vulnerability with respect to the main types of accidents, the main types of accidents were
determined in [
At the 3rd stage, the evaluation of the criteria was carried out with the help of the AHP, which was
implemented using the expert survey web form, as well as the developed algorithm that translated the
experts' assessments into a pairwise comparison matrix for the further implementation of the
evaluation of the criteria in each group according to the steps of the AHP [
At the 4th stage, a secondary space of criteria was developed by reducing the primary space. The selection of criteria that fall from the primary space to the secondary space for each type of accident j was made using the threshold processing of the corresponding values of the weights of the criteria wji in the corresponding group: we'ji = weij E ;i = 1, NE ; j = 1,3 ws'ji = wsij S ;i = 1, NS ; j = 1,3 wa'ji = waij A;i = 1, N A; j = 1,3
As threshold values, the authors adopted the following values for each group of criteria
E = S = A = 0.1 . At the same time, the group of criteria determining possible lost lives as a result of
an accident is not reduced in accordance with the principles of the project [
Then the secondary space of GS vulnerability assessment criteria will have the following form:
Spsf = LLji , we'ji ; E *ji , ws'ji ; S *ji , wa'ji ; A*ji , where we'ji , ws'ji , wa'ji these are the weights of the relevant criteria in the economic E *ji , social S *ji and environmental group A*ji respectively. Therefore, the groups of criteria in the secondary space after the completion of the 4th stage of the method take the following form: (2) (3) • • • • • • •
Lost lives LL = LL1, LL2 , LL3 , LL4 , LL5 , LL6 , LL7 , LL8 , LL9 ;
S* = S1*, S2*, S3*, S4*, S5* ;
E* = E1*, E2*, E3*, E4*, E5*, E6* ;
A* = A1*, A2*, A3*, A4*, A5*, A6* .
At the 5th stage, the authors collected data based on the formed secondary space of criteria for the GS network of a certain settlement from public data. Fragments of the received grey data are partially shown in Table 1, and for security reasons, we do not assign a coordinate reference to a specific GS. At this stage, we did not collect data on the criteria of lost lives. and analysis of data to obtain a further assessment of the GSs vulnerability:
The variety of required data: the data required to assess the values of EESC are very diverse, which makes it impossible to find all the data in one source; some data require knowledge of GS features, staff qualifications, etc.; The need for manual data collection: most of the time it is necessary to search and analyse data by an interested person, which can lead to the search for a large amount of sometimes redundant information and distortion of data; Unstructured data: data obtained during search and analysis is mostly very unstructured and detailed, which complicates data analysis and processing.
At the 6th shown in Table 1 in accordance with the needs of the construction of the subsequent DSS to assess the vulnerability of the gas station to the main types of accidents. According to the defined values of the criterion, the data is given a code value between 0 and 1, where 0 corresponds to the least unfavourable value of the criterion, and 1 to the most dangerous. A fragment of the data coding table for some criteria is presented in Table 2, the full coding table for all criteria of the secondary space, excluding lives lost, can be found in [15]. 0.7,GS fueltypes 6 + 0.3 GSgasfuel
At the 7th stage, for obtaining the vector of weights of the relevant groups of EESC with the help of AHP, the authors proceeded from the following considerations. Since the secondary criteria space had 17 components, to simplify the implementation of the AHP, the data obtained by British colleagues on the project were used, namely, with the help of the classic AHP, they compared the relevant groups of criteria with each other, thus obtaining weight coefficients for the group [16]. The results of this comparison and the corresponding weight coefficients are shown in Table 3.
Next, we sum up the received weights for each type of accident j to obtain the value W j for the following normalization of the weights of the criteria, which will allow us to obtain a vector of weights for each type of accident with a total value of 1:
* N* * Wj = NE we'ji + S ws'ji + NA wa'ji , j = 1,3 ; i=1 i=1 i=1 (5) we*ij = wWe'jji , ws*ij = Wws'jji , wa*ij = wWa'jji . (6)
The values of criteria weight vectors obtained at the 7th stage for each type of accident are shown in Table 4. At the 8th stage, considering the previous stages 1-7, we will propose the following general model for assessing the GSs vulnerability:
Nac NE* NS* N*A j=1 i=1 we*ji e fi + ws*ji s fi + wa*ji a fi Vf = i=1 i=1 , (7)
Nac where V f assessment of the GS vulnerability with the identifier f; Nac total number of main types of accidents (in the study j = 3); NE* , NS* , N A* total number of EESC respectively; we*ji , ws*ji , wa*ji the weight of the EESC for accident scenario respectively (Table 4); e fi , s fi , a fi score i of EESC for f GS for scale from 0 to 1 respectively (Table 2 [18]).
Since the value of the GS vulnerability assessment obtained using the formula (7) is expressed as a
decimal number in the range [
Fuzzy variables from the term set T are also defined as tuples of the form:
T = i ,Vi , Ai ,i = 1,3 , (8) (9) where: i name of the fuzzy variable ( 1
high vulnerability );
Vi scope of definition, for all 3 variables V = [
Fuzzy sets A1 , A2 and A3 are proposed to represent in the form of trapezoidal membership functions fTi as a first approximation due to their simplicity, frequency of use in scientific researches, and the fact that no research was conducted on the spread (Gaussian and others) of the GSs estimations due to the small size of the studied set of objects. ctions fTi for fuzzy variables i with the following parameters: • • • fT1 (Vf , 0, 0, 0.3, 0.4) fT2 (Vf , 0.3, 0.4, 0.6, 0.7) fT3 (Vf , 0.6, 0.7,1,1) for 1 low vulnerability ; for 3 high vulnerability .
for 2 medium vulnerability ;
The corresponding membership functions fTi can be displayed graphically in Fig. 2. The fuzzy variable low vulnerability is shown in black, medium vulnerability in blue, and high vulnerability in green.
After substituting the value of V f the degree to which the vulnerability assessment belongs to one of the 3 fuzzy variables can be calculated (respectively Ai (Vf ) ge [0.6; 0.7] set the fuzziness in determining whether the assessment of the GS belongs to the relevant variables.
In accordance with the above considerations, at the 10th stage, the knowledge base for assessing the GSs vulnerability to the occurrence of accidents of the main types is formed, which in turn consists of a filled-in database about the GSs network, which will be further filled in accordance with stages 5 6, and will also contain a database rules for deriving the interpreted result of GS vulnerability assessment. In accordance with the above considerations, we can propose the following rule base for the output of the interpreted result (considering the number of proposed terms, it can be expanded in the future): R2: if fTi = 0 R3: if N ( fTi 0) 1 th
f
Ti2 % belongs to the do not display message; he second ith
th • • • f
Below, after deriving the interpreted result of the GS vulnerability assessment to the main types of accidents, the following messages can be issued that characterize the fuzzy variables of the GS vulnerability and can serve as a basis for decision-making by stakeholders:
ow vulnerability means that in the event of a possible accident at the assessed GS, the scale of adverse consequences (economic, social and environmental) may be low and meet a certain acceptable level;
edium vulnerability means that in the event of a possible accident at the assessed GS, the scale of adverse consequences (economic, social and environmental) may be greater than the average level and may serve as a basis for conducting an examination of the assessed GS for compliance with all necessary regulations by regulatory authorities and taking necessary measures;
igh vulnerability means that in the event of a possible accident at the assessed GS, the scale of adverse consequences (economic, social and environmental) may be large, i.e., such that they may cause significant impact, and in this case, inspection of the assessed GS for compliance with all necessary regulations by regulatory authorities is recommended to take measures to reduce threats or close the GS.
An example of the screen form for deriving the interpretation of the vulnerability of a GS, where the assessment of the GS vulnerability belongs to 2 fuzzy variables at once, and therefore is subject to the rule R3 of the output of the interpreted result, is shown in Fig. 3.
As part of the implementation of the project, data on 17 GSs of a certain settlement were collected from open sources. Further, the obtained data were coded according to the coding table in [18]. The result of the received coded values of the GS vulnerability criteria with the help of intelligent IT is shown in Table 5 (full in [15]). After coding the GS data, showed in Table 5, by using model (7), estimates of GS vulnerability to the main accident types were calculated, the value of which is displayed in the last column of the Table 5 (V). Based on the results of GS vulnerability assessments, we will plot the distribution of assessment parameters depending on their location in Fig. 4. The parameter x is the normalized value of the latitude (y the longitude respectively) on which the GS is located. GSs are located on the graph depending on the coordinates and have a colour depending on the vulnerability assessment parameter to the main types of accidents.
From Fig. 4, GSs can be divided into the following classes when comparing each other: •
Dark blue and light blue (4 objects)
has the least vulnerability; •
Green (3 objects) Yellow (3 objects) has a vulnerability below average; has medium vulnerability; Orange (4 objects)
has an above-average vulnerability; Red and brown (3 objects)
has high vulnerability.
Let's consider the interpretation of the GS assessment, dividing it into the categories using fuzzy variables described in Fig. GSs on graph in Fig. 5 in the form of point values on the corresponding graphs of membership functions.
From Fig. 5 we can see, that studied GSs have the following distribution by fuzzy variables of GS vulnerability assessment: 1 GS has low vulnerability, 13 GSs have medium vulnerability, 2 GSs have medium and high vulnerability with different degrees, 1 GS has high vulnerability.
As a result of the research, an intelligent IT was developed for multi-criteria vulnerability assessment of GSs to the main types of accidents. To build it the 10 consecutive stages are proposed and described above, which allows for the creation of a DSS, which will allow numerical assessments of GS vulnerability based on the collected data to be converted into categorical ones using FL for further interpretation by decision-makers.
Among the advantages of using the proposed intelligent IT, it should be noted the increased efficiency of the preliminary analysis of the GSs vulnerability, since the developed IT not only increases the speed of data analysis and data collection, but also provides reasonable assessments of the GSs vulnerability, which gives recommendations for decision-makers. It is also worth noting that the proposed approach with further development and implementation in practice, can serve as a methodological basis for assessing the vulnerability of more complex potentially hazardous facilities, especially in their combination. As a disadvantage of the mentioned approach, its partial nonuniversality should be noted, since accidents at potentially hazardous facilities and their combinations may be different in nature; the potential complexity of forming an initial model of the vulnerability of a technological object, which may require the involvement of experienced highly specialized specialists, which is a time- and material-consuming task; subjectivity in decision-making, especially when they are based on expert assessments, which can cause bias and affect the objectivity of the results. Also, due to the limited implementation time and financial capabilities of the project, the group of criteria of lost lives in a possible accident was excluded from the consideration of the GS vulnerability model, but this can serve as a basis for further research and improvement of the considered model.
In general, the developed intelligent IT can serve as a basis for a preliminary review of existing GSs and a preliminary assessment of their vulnerability to the main types of accidents, which can serve as a basis for making decisions about an extended assessment and limitation of their activity or closing the GS. This study may be of interest to a wide range of stakeholders, including regulatory bodies, the population, public organizations, etc.
This project was made possible through the UK-Ukraine twinning grants scheme, funded by Research England with the support of Universities UK International and UK Research and Innovation.
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