Identification of Territorial Vulnerability Index based on Hierarchical and
Heuristic Models using SOA
Wilmer David Oidor Bolaños
Luis Alejandro Rodriguez Torres
Universidad Católica de Colombia
Bogotá, Colombia
e-mail: [email protected]
Universidad Católica de Colombia
Bogotá, Colombia
e-mail: [email protected]
Abstract— In the project design and development of a Web
service is performed by each of the decision -making models
(AHP , Fuzzy AHP , ELECTRE and PROMETHEE ) , which
would be responsible for processing field data in the first phase
of the project " Retrospective of Natural Disasters in Colombia
As Input for Building a Decision Support System " , conducted
through surveys , interviews formats , workshops and analysis
methodologies . The data were processed according to the 4
decision models, generating a final outcome indicator
territorial vulnerability.
To proceed with the analysis and further development each
web services, first a literature review of each of the models
above decision making was carried out to identify the
structure, methodology and performance. Further analyzed
and identified the data obtained in the field in the first phase to
fulfill their function as input information for each model and
the corresponding algorithm treatment process it and generate
the final result.
Keywords- Decision Making, Decision Theory, Disaster
Prevention, Natural Disaster, Systems Design.
I.
INTRODUCTION
Currently worldwide there are many systems or
technologies that are responsible for informing and bringing
the consolidated few natural disasters have caused and what
places have occurred, but do not have a system to report or
provide the information necessary to know what vulnerable
is some territory and what might be the possible actions to
take according to the degree or level of vulnerability.
In this regard and in order to strengthen risk management
in the country, this research responds to the second phase of
the project entitled "Retrospective of natural disasters in
Colombia as input for the construction of a decision support
system", where necessary to advance a decision support also
incorporate response mechanisms in the territory in the short,
medium and long term system. Specifically contributing to
the prioritization of intervention actions when a natural
disaster occurs.
In the first phase of a system of indicators which aimed
to review the involvement in the territorial system after a
natural disaster and found that dimensions such as sociocultural and political institutions, needed special treatment is
proposed. This considering that information to support the
system of indicators should be generated through field
surveys formats interviews, workshops and analysis
methodologies.
For this, during the second phase aims to build a system
that generates greater impacts, building different strategies
for information such as the welfare and development,
community organizing, psychological, and cultural beliefs
that identify a territory. Additionally, the design of the data
model of the first phase was achieved conceptually
structuring a model where the input data are indicators and
output alarm levels of involvement in the territorial system,
the latter intended to generate signals to people skilled in
making decisions about which dimensions concentrate
prevention plans, emergency mitigation and reconstruction.
The problem is how to identify the territorial
vulnerability index using data and indicators collected and
analyzed in the field of stage I of the project "Retrospective
of Natural Disasters in Colombia As Input For Building a
Decision Support System" .
According to the above account raises the following
research question that will lead the development of this
proposal:
What is the treatment and adaptation will have to give the
decision-making models to build and calculate the territorial
vulnerability index, based on the data collected?
II.
RELATED WORK
A. Prioritized Multi-Criteria Decision Making Based on
the Idea of PROMETHEE.
This article shows the concept to develop a method based
on the idea of Promethee model that compensates the
imperfection of aggregation operators to prioritize all
possible situations according to the pairwise comparison
when implementing existing models multicriteria decision
because the existing models or multi techniques are very
useful to provide solutions to complex problems, but these
models do not take into account all the possible connections
between the various criteria [1].
Current research in multi-criteria models mainly focus on
how to add information regarding the assessment of
prioritization criteria including how to build prioritized
aggregation operators, but often do not take into account the
type of prioritization or the order of priority [1].
The objective is to develop a new multi precedence over
the idea of promethee model is to compare alternatives in
pairs with respect to criteria one by one, using as a first step
the definition of the preference function, then calculate the
index of preference, it is here where preference function or
revision levels are expected or influence of the decision
maker on alternative and / or criteria followed this
intuitionistic preference ratio (combination of technology
decision making is constructed and used Fuzzy theory)
containing the levels of certainty expressed in a matrix, and
two indexes of preference for the alternative, an average is
obtained; Finally a range of alternative preferred ratio is
obtained , thus obtaining a vector of classification that can be
used for ranking the alternatives.
B. Revised PROMETHEE II for Improving Efficiency in
Emergency Response
This article proposes to implement several steps of
traditional Promethee II model for the calculation time and
increase the number of incidents emergency management
plan, to strengthen the emergency response system and
strengthen public dialogue. [2].
In all studies for emergency management have been used
different models or theories of multi-criteria decisionmaking, but rarely has been used promethee model or theory,
as it is very difficult to meet the timeliness requirements
management emergency due to the multiple steps, the large
number of calculations and comparisons of the traditional
algorithm promethee II.
C. Multicriterion analysis of a vegetation management
problem using ELECTRE II. Appl. Math. Modelling
One objective of this work done, is to use the ranking or
classification Electre II of management actions watershed,
actions, alternatives and their estimated impacts that may
have a vegetation specifies oriented to increasing water
production a forested watershed of 38.8 square miles in the
White Mountains of central Arizona. [3].
The alternatives of the problem are evaluated on seven
criteria: The wood and fodder, agriculture, water supply,
maintenance, floods, hydropower generation and reservoirbased recreation. Stochastic models of precipitation
structured around a deterministic watershed model and a
system of hypothetical reservoir, were used in the computer
simulation to evaluate these management options in terms of
their respective impacts on the criteria.
D.
Land acquisition and resettlement action plan
(LARAP) of Dam Project using Analytical Hierarchical
Process (AHP): A case study in Mujur Dam, Lombok
Tengah District-West Nusa Tenggara, Indonesia.
This article is published as the AHP model is used to
decide the best location for the people who are in the area
where you want to build a water dam, taking into account the
land acquisition plan of the local government of the people
of Mujur . As expected in the dam project area recovers to be
more productive in order to increase the prosperity of the
people. However, as this project often leads to other
problems, and the most striking is the resettlement of people.
Therefore, the study of land acquisition and Resettlement
Action Plan (LARAP) should be implemented prior to
construction of a dam in order to have a precise project
disadvantages consideration of the advantages and those
affected [4].
The study seeks to find relocation advantages and
disadvantages suffered by those affected, which must
consider and take into account many factors, socioeconomic, comprehensive planning of the acquittal of land,
resettlement, and schema compensation. The study requires
only that the resettlement areas should be as close as possible
to the areas of acquisition and displaced persons should be
satisfied, generating great community involvement, to
minimize environmental risks, and finally that people have
good access transport.
E. Framework to measure relative performance of Indian
technical institutions using integrated fuzzy AHP and
COPRAS methodology.
The theme that relates to the article due to propose a
framework to measure the performance of technical
education in India, because it crosses many challenges today
because of globalization and liberalization of the economy in
that country. For the study are based on data collected in
2007 and 2008 7 institutes of technology preference criteria
analyzing stakeholder model using the combination of AHP
and FUZZY multiattribute proportional method to the
evaluation of alternatives (COPRAS).
The performance of the technical institutions in the
absolute sense, it is very difficult to measure. There are many
factors / criteria / attributes / objectives affect the
performance of institutions and the result of the measurement
is very sensitive to the selection criteria [5]. You have to
carefully consider when making the selection of criteria to
measure the performance of these educational institutions as
there are criteria that depend on others to do this are based on
the study of historical data pertaining to education models in
India and the opinion of experts in the field.
III.
METHODOLOGY
Based on information from the document review and
analysis of the models making multi -criteria decision,
proceed with the application of concepts related to systems
engineering for the project implementation. The project is
structured around the steps defined in the agile development
methodology AUP (Agile Unified Process), showing the
role, phases and components thereof.
UPA methodology "covers, plus a set of procedures and
tools designed to correct modeling of the business during the
life cycle of software development, a framework of good
practice for the construction phase of the software " [6 ] .
The deliverables required by this methodology was
adapted to reality and life time of the project and are also
relevant to the nature of the software solution; together with
the existence of a greater number of open source tools, aimed
at modeling systems generating UML artifacts needed for the
analysis and design phases of the web services.
The selected agile development methodology has four
stages throughout the process. The stages are defined in the
following items structured as follows:




Initiation: It corresponds to the theme developed in
the application of decision models for identifying
the vulnerability index.
Preparation: the class diagram and sequence is
developed.
Construction System: functioning and structure
associated to Web services are explained.
System transition: the process of developing tests
that apply to Web services is displayed.
IV. IMPLEMENTATION OF DECISION MODELS
FOR IDENTIFICATION OF VULNERABILITY INDEX.
The operation of each of the decision-making models are
used to make the calculation of the territorial vulnerability.
A. Model Hierarchical Analysis Process (AHP).
Here's a simple operation of AHP model to calculate the
Territorial Vulnerability Index.
1) Read File. Sort by territorial dimension, for storage
in a data matrix (criteria), then read all the variables
belonging to one dimension and store them in a data matrix
(sub) by distributing the information as a hierarchical tree
as follows:

Objective: Index of territorial vulnerability.

Criteria: Dimensions (1, 2, 3, 4, 5 ...)
 Sub-criteria: Variables belonging to each territorial
dimension
2) Square MAtrix Criteria. Criteria which in this case
corresponds to the territorial dimensions and a square matrix
formed from the elements are selected.
3) Comparison Par. A paired comparison of a
dimension relative to the other, indicating how important
criterion is performed against the other according to the
scale of Saaty, which is listed in Table I.
TABLE I.
Value
compared
pair ij
1
3
5
7
9
SAATY SCALE
Interpretation
The criterion i and j are equally important criterion
The criterion i is slightly more important than j
The criterion i is strongly more important than j
The criterion i is very strongly more important than j
The criterion i is absolutely more important than j
Others
Values
Explanation
2,4,6,8
Intermediate values between two adjacent judgments
used
as consensus values between two trials.
increment
0,1
Intermediate values for finer gradations to trials
(For example 7.3 is a valid entry).
The scale according to a preference value or level of
importance with respect to other criteria being compared
is assigned, as shown in Figure 1.
TERRITORIAL
DIMENSION
PoliticalInstitutional
Enviro
nmental
Sociocultural
economic
productive
built
(urban regional)
PoliticalInstitutional
1
1/3
5
1/5
1/7
environmental
3
1
2
4
3
1/5
1/2
1
5
1/7
economic
productive
5
1/4
1/5
1
1/6
built
(urban - regional)
7
1/3
7
6
1
Sociocultural
Figure 1. Matrix values as criteria Saaty scale.
From this matrix can be deducted that the diagonal
consists of number 1, and which represents the comparison
between the same criterion, only the values of the upper
diagonal are filled, and that the reciprocity principle diagonal
down are inverted values, that means that if for example the
dimension is compared against Political-Institutional
Environmental considering a preference with a value of 1/3
then the Environmental dimension compared regarding the
Political-Institutional its value is 3.
4) Normalize Matrix. The matrix is normalized by
dividing each element i, j between the total sum of its
respective column, for example the sum of column 1
(political and institutional) is 16.20, this value is divided
between each item in column 1 that shown in Figure 1..
5) Get Priority Vector. After obtaining the normalized
matrix, the relative priority of each of the elements being
compared is calculated by averaging each of the rows of the
normalized matrix, as shown in Figure 2..
Dimensión
D1
D2
D3
D4
D5
Priority
D1
5/81
1/7
25/76
1/81
1/32
4/35
D2
5/27
31/75
5/38
20/81
60/89
1/3
D3
1/60
6/29
5/76
25/81
1/32
1/8
D4
25/8
1
3/29
1/76
5/81
1/28
7/67
D5
35/8
1
3/22
35/76
10/27
20/89
13/40
Figure 2. Matrix with Criteria Priority Vector.
In the case where there are hierarchies and sub-criteria,
the priorities of the criteria are determined by the goal and
have the greatest values. Subsequently matrices subcriterion
comparisons that are related to a given criterion is
performed. The relative priorities of each sub-criterion is
obtained and to determine how they affect the priority
objective of each sub-criterion is multiplied by the priority
criteria.
Repeats the same process of the criteria for the sub, as
follows:
Forming square matrix of variables (sub-criteria) as the
dimension to which the variable belongs performing pairwise
comparison according to the Saaty scale.
PolíticalInstitutional
S1
S1
S2
1
S2
5
Sociocultural
S1
S1
1/4
Built
S1
S1
S2
S1
1/5
S2
1
EconómicProductive
S2
1
S2
Environmental
S1
4
S2
1
S1
S2
1
7
1/7
1
S1
S2
1
1/3
3
1
S2
1
4
1/4
1
Figure 3. Matrix Matrix Subcriteria (Variables) Criterion (Dimension)
After assigning comparison values between pairs of
elements, we proceed to normalize each of the matrices, and
then obtain the relative priority of each of the elements
compared, averaging each row of the normalized matrix.
D1
S1
S2
D3
S1
S2
D5
S1
S2
S1
S2
Priority
0,16
0,16
0,16
0,83
0,83
S1
S2
0,8
0,8
0,83
Priority
0,8
0,2
0,2
0,2
S1
S2
0,8
0,8
0,8
0,2
0,2
0,2
D2
D2
S1
S2
D4
S1
S2
S1
S2
Priority
0,87
0,87
0,875
0,12
0,12
0,125
S1
S2
0,25
0,25
0,25
0,75
0,75
0,75
Priority
Priority
Figure 4. Matrix Subcriteria (Variables) with Priority Vector.
Then he proceeds to multiply each priority sub
(variables) for the priority value of the dimension of the
corresponding.
6) Calculate Territorial Vunerability Índex.
To
calculate the rate of territorial vulnerability, the variable is
selected with greater weight or value within each dimension.
B. Model Fuzzy Hierarchical Analysis Process (AHP
Fuzzy).
The AHP requires comparisons and considerations about
the criteria and alternatives are represented in a precise
number, and thus develop the preference matrix in which the
method is based to select the best alternative [6 ] . To deal
with the vagueness and subjectivity of human judgment and
with multiple criteria, there is a theory called Fuzzy
Hierarchy Process Analysis, which is a combination of the
Hierarchical Analysis Process (AHP) with fuzzy logic
(Fuzzy Logic).
According Büyüközkan as expressed in [6] “those
responsible for making decisions usually feel better by
presenting their judgments as a range, rather than give a
precise and fixed value. This is because he, she or they, are
unable to explain their preferences, given the diffuse nature
of the processes of comparison. "
In making decisions , the judgments made by the
decision makers agents regarding alternatives and criteria ,
can be converted into fuzzy numbers which are called fuzzy
triangular numbers by graphing , calculating the importance
weights using the AHP ; these numbers are used to construct
the pairwise comparison matrix of AHP .
In the conventional AHP, the pairwise comparison is
made using a nine-point scale, which represents the
judgments or preferences of decision-makers from different
options. Although this discrete scale from one to nine, is
simple and easy to use, does not take into account the
uncertainty associated with human judgments. The linguistic
terms that people use to express their feelings and opinions,
are vague, subjective, which is why the AHP combined with
fuzzy logic to represent linguistic judgments, and the theory
of fuzzy sets is used to work with ambiguity in a system.
The steps required for the implementation of the
methodology are:
1) Desarroll Hierachical for Development Criteria and
Subcriteria. The evaluation criteria and sub-criteria should
be structured in different levels of hierarchy according to
AHP raising. For this we must construct a tree diagram that
summarizes the relationships between the components of the
problem to be solved. In the upper schema must always
include the main objective or goal and at lower levels
should include the set of criteria and sub-criteria..
2) Representación Fuzzy Representation Trial. After
constructing the hierarchy, it should be converting the Saaty
scale on a scale of triangular fuzzy numbers, according to
the Table. Triangular numbers M1, M3, M5, M7 and M9 are
used to represent the trials from preferred or equal to
extremely important, and M2, M4, M6 and M8 represent
intermediate values..
TABLE II.
FUZZY AHP SCALE COMPARISON
scale
Saaty
scale
fuzzy
representation
Verbal scale
interpretation
1
(1,1,2)
M1
Equally important
from both
elements
The two elements
likewise contribute
form the target
Moderate
importance of
element of
another
Experience and
judgment
Slightly favor an
element on the
other.
3
(2,3,4)
M3
5
(4,5,6)
M5
7
(6,7,8)
M7
Importance of a
strong
element on the
other
Very strong
importance of
element of
another
One of the
elements is
strongly favored
One of the
elements is
strongly dominant
9
(8,9,9)
M9
A paramount
element of
another
The evidence
favoring
one of the
elements
is the highest order
of
assertion
2,4,6,8
(1,2,3)
(3,4,5)
(5,6,7)
(7,8,9)
M2,M4
M6,M8
Intermediate
values
Used for
intermediate
judgments
Source: [8].
3) Construction of fuzzy judgment for AHP. Based on
the hierarchy built in step one and step two fuzzy scale is
applicable to the construction of the parent trial. The
hierarchy of criteria and alternatives is the subject of
pairwise comparison for AHP. After building the nest, the
team responsible for making the decision has to compare the
elements in given levels to estimate their relative
importance in relation to the top-level element.
To do this, the triangular numbers (M1 - M9) is used to
express preferences between different criteria with respect
to the goal. For example, if i think that the element is
strongly preferred to item j with respect to the goal, then a ij
= (4, 5, 6) rating is set; Comparison of element j with
respect to element i must be reversed so that the judgment is
consistent and should express aji = (1/6, 1/5, 1/4). From
these scores the first comparison matrix is obtained by pairs
between criteria with respect to the goal.
Apart from this matrix must be constructed matrices
pairwise comparison for each of the levels of the tree
hierarchy, that is, comparison matrices between the sub with
respect to each of the criteria and the alternatives in relation
to the sub . But the dynamic construction is the same as
above.
4) Math Operations. Once the pairwise comparison
matrices are constructed, the calculations must be made
relevant to the development of the methodology, which are:
the calculation of the weight vectors for each level of the
hierarchy using the extended analysis and comparison
principles of fuzzy numbers.
C. Model Elimination et Choixtraduisant the Realité
(ELECTRE)
The process to be performed when using this model or
technique must take into account six steps needed to end up
having a ranking of alternatives, which are:
1) Defining the problem(finite set of alternatives,
criteria, weights). It must identify what will make the
alternatives is a finite set and evaluation criteria which aim
to prioritize each of the alternatives, where the alternatives
are the rows of our matrix and criteria columns, obtaining
the matrix criteria alternatives NxM..
The weights associated with each of the criteria and scales
measuring qualitative and / or quantitative. Remember that
not all criteria necessarily have the same specific weight to
the decision maker, so you have to assign a value. Also, not
all aspects can be measured with the same measurement
scale and thus may also have different ranges.
2) Filling the matrix-criteria alternatives. Following the
evaluations for each of the alternatives based on the various
criteria set are captured. These can be obtained by
conducting various studies, such as surveys, expert opinion,
simulations, among others.
3) Generación de la matriz de concordancia (medida
ordinal). Teniendo las evaluaciones e de la matriz de
alternativas- criterios se construye la matriz de
concordancia. Esta matriz expresa qué tanta preferencia
hubo en las evaluaciones de las alternativas con base en los
criterios establecidos.
4) Generatión discordance matrix (cardinal measure).
From data-alternatives matrix mismatch criteria matrix is
constructed. This matrix expresses how much indifference
was in evaluations of the alternatives based on the criteria.
5) Analysis on classification relationships. An analysis
of the information on classification using the concordance
and discordance matrices using the following rule is:
An element ak R (on classified or dominates) to another if
it meets:
 There is an indicator of most criteria for which we
can say that k is at least as good as the.
(Concordance index).
 No criteria disagreed with this mostly shows a too
strong superiority that is better than ak.
(Discordance index).
To understand the concept of superiority or two most
known parameters are set: matching parameter p, and q
parameter mismatch.
6) Ranking of the alternatives. Finally, after performing
sensitivity analysis with several different pairs of parameters
p and q, and taking several parametric graphs associated
with each of the analyzes, a conjunction of them is made
and alternatives are ranked, expressing a graph synthesis..
D. Model Preference Ranking Organization Method for
Enrichment Evaluations (PROMETHEE)
The following process describes the actions and
procedures necessary for data processing regardless of the
form of how the data were obtained, based nxm matrix
where n is the number of dimensions and m the amount of
data in that dimension . The model is divided into the
following stages:
1) Calculate Differences Matrix. A comparison of data
was performed by comparing pairs a row of the matrix with
all the others, here you should consider whether each of the
variables m are to be maximized or minimized..
Should Maximize, If the data to compare is greater than
or equal compared to the data, we take the data to compare.
If MINIMIZE; If the data to be compared is less than or
equal compared to the data, the data is stored to compare.
As a result the matrix all the differences, where each row of
the matrix are pairwise comparisons of the original matrix is
obtained.
2) Calculate Preference Function. At this stage the
difference matrix is used and one of the six criteria of
preference applies to each of the columns of the array or
variables m, it should be noted that the value 0 means that
the data is irrelevant and that one is strictly preferred value.
Within this stage is used each q, p, σ thresholds according to
the type of function or preference criterion used. Results in a
matrix is obtained with outcomes between 0 and 1.
3) Calculate Preferred Indices. This is when using
weights or importance levels established by the decision
maker is made, the procedure to be performed is to take the
preference matrix and multiply each data value of each
assigned weight. For each row of the matrix preferably a
sum of their weight and multiplied data is performed and is
stored in the preference matrix index. As a result a square
matrix with zeros in the diagonal and preferably indicies of
each row of the matrix is obtained preferably..
4) Overcoming Calculate Positive Cash Flow. An array
is created from the values of the array indices preferably by
adding the values of each of the rows of the matrix and
placing the value of the sum at each array dimensions. At
the end an arrangement of a dimension n according to the
number of row of the matrix is obtained preference index.
The aim of this process is to obtain the positive expressing
outranking flow as an alternative to dominate all other.
5) Calculate Flow Negative improvement. An array is
created from the values of the array indices preferably by
adding the values of each of the columns of the matrix and
placing the value of the sum in each array dimensions. At
the end of an array of dimension m according to the number
of columns of the matrix preference index is obtained. The
aim of this process is to obtain the negative expressing
outranking flow as an alternative to it sobrepujada or
exceeded by all the others.
6) Get Net Flow of Accomplishment or Full Ranking. Is
obtained by performing a pre subtracted from positive to
negative flow minus flow, obtaining a settlement of the
amount of alternatives and their value to the decision
making process according to the concerns as PROMETHEE
II model, which states that all alternatives are comparable
and that the resultant information may be moot because
information is lost by considering only the differences. The
higher the net flow is the best alternative.
E. Requirements Analysis.
After making a theoretical review of the 4 models of
multiple criteria decision making (AHP, Fuzzy AHP, and
Promethee Electre) proceeds to establish the necessary
requirements to be met by each of the web services for the
calculation of the territorial vulnerability noteworthy are a
total of 4 web services each corresponding to a model of
decision making, in Table III the list of associated functional
requirements shown and identified with the design, creation
and operation of web services to calculate territorial
vulnerability index.
TABLE III.
FUNCTIONAL REQUIREMENTS
ID
Name
RF01
Read XML file
Process information and generate
vulnerability index from the AHP model
Process information and generate
vulnerability index from the model
PROMETHEE II
Process information and generate
vulnerability index from FUZZY AHP
model
Process information and generate
vulnerability index from the model
ELECTRE III
RF02
RF03
RF04
RF05
RF06
Genérate XML file.
F. Use Case Diagrams.
Following the process of AUP methodology (Agile
Unified Process) is proposed for the development of web
services implementing different diagrams in the UML
standard. In this project the use case diagrams, class
diagrams and sequence diagrams are provided.
1) Diagram Use Case Diagram Use Case is defined by
the abstracted functional requirements of the document
review of each decision making models adapted to calculate
the index of territorial vulnerability.
Figure 6. Sequence Diagram.
Figure 5. Use Case Diagram.
V. MODEL FOR THE CALCULATION OF
VULNERABILITY INDEX FROM HIERARCHICAL
DECISION MODELS AND HEURISTICS.
B. Static Model System.
The class diagram is responsible to describe and / or
show the structure of the system showing its classes,
interfaces and / or objects. Within this type of model
attributes, associations and generalizations of each class is
also shown [10].
The following class diagram shows the general structure
is developed as web services. In the general structure shown
three packages, webService wherein the method is exposed
in the web service with their respective interfaces, the
package that contains useful utilities web service that are
read and create the XML, and package model, in which all
classes are associated with each model in Figure 7 the
overview diagram of all classes to develop web services
shown.
After the analysis regarding the operation of models in
multicriteria decision-making in order to calculate the rate of
territorial vulnerability to further define the functional
requirements and use cases associated with the development
of web services, we proceed to propose the model dynamic
and static system.
A. Dynamic System Model.
The sequence diagram is responsible for showing the
steps, the process or interaction between objects, which
represents the sequence of messages between instances of
classes, components, subsystems and actors [10].
The sequence diagram is depicted in Figure 6 shows the
overall interaction process that makes each of the web
services to be developed, it should be noted that according to
the type of model (AHP, FUZZY AHP, PROMETHEE,
ELECTRE), there may be more than one iteration between
classes "Model", a process that does not alter the overall
process and the end result or purpose of the web service.
Figure 7. Class Diagram.
VI. PROTOTIPO PARA EL CÁLCULO DEL ÍNDICE
DE VULNERABILIDAD A PARTIR DE MODELOS DE
DECISION MULTICRITERIO.
Within the process for calculating the index of territorial
vulnerability shows a view as developed in Figure 8, for this
run each of the web services associated to each decision
models. In this view or screen is obtained as a result the
XML generated by the application in text format.
Figure 10. AHP model execution.
Figure 8. Initial Vista Web Services.
In the normal web services to run through sight or front
end developed in order to test the performance of each of the
web services process, view some validations are performed,
as shown in Figure 9, between which they are not selected
model or web service to use or errors in reading or reading
the XML file does not contain the name envio.xml regardless
of uppercase and / or lowercase file path.
Figure 11. XML result of AHP model execution.
To perform the test operation of web services associated
with decision models already known (AHP, Fuzzy AHP,
Electre, Promethee) and view or front-end developed to
show the execution of web services installed or deployed
each one of the component services and view an application
server (Oracle WebLogic). Keep in mind that these web
services can be installed on another application server
following the installation manual provided by each developer
and / or manufacturer.
VII. CONCLUSIONS
Figure 9. Validations screen WS
Below successful by running each of the web services
and the response result of each sample, showing in the result,
the rate of territorial vulnerability and vulnerability index for
each of the dimensions. In Figure 10 the execution of one of
the web services associated with one decision models and
XML shown.
The treatment was given to the data collected and
analyzed in the first phase of "Retrospective of Natural
Disasters in Colombia As Input For Building a Decision
Support System" project was key to be used as input to the
system developed, as it has different types of data (index,
indicators), so it became necessary to investigate what each
meant.
The calculation of the territorial vulnerability is evident
with each of the implemented algorithms that represent the
entities and attributes of each model required to show the
final result, supported and based on the documentation of
each decision making model.
The structure of the proposed architecture for the
development of Web services, intended as the basis for
future development and implementation of other decisionmaking models, since there are many. This aims to provide
diversity to the person skilled in decision making to compare
and evaluate the results generated by various models and
analyze the territorial vulnerability index generated by each
of them, in order to take appropriate actions to help mitigate
the impact of natural disasters in Colombia.
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