Evaluating Post-Accident Nuclear Risk Using GIS and Rough Set Theory

Input2012 - Cagliari 11 May 2012
-
EVALUATING POST-ACCIDENT
NUCLEAR RISK BY COUPLING GIS
AND ROUGH SET THEORY
1 Salem Chakhar University of Laval, Canada
Clara Pusceddu University of Sassary – Faculty of Architecture
of Alghero, Italy
Ines Saad, University of Picardie, France

INTRODUCTION

The management of the consequences of a major nuclear

accident necessarily involves the consideration of multiple
criteria in order to ensure sustainable development in
areas that might be affected.

Cagliari 11 May 2012
Furthermore, the management of the consequences of a
major nuclear accident requires a multidisciplinary
approach to produce a sustainable response to the
environmental, economic and social problems linked to the
various local intricacies.

2

OBJECTIVE

Propose a Multicriteria Evaluation approach for

characterizing the different districts of the affected area in
terms of their vulnerability levels while taking into account
multiple stakeholders with contradictory objectives and

priorities.

The proposed approach is composed of 4 phases:
1. identifying the stakes involved,
2. identification of representative criteria,
3. quantifying criteria scores, and
4. group multicriteria classification.
3

FOCUSING ON 4 PHASE…. (CHAKHAR…)

It requires the use of an adequate technique to combine the

perspectives of different stakeholders.

We adopted the output-oriented strategy (Dias and

Climaco, 2000) to combine these perspectives. This strategy
works as follows:
1. first, each stakeholder performs her/his individual
classification; then

2. an appropriate aggregation operator is used to
combine the individual classifications into a collective
one. 4

A DECISION SUPPORT SYSTEM DESIGN….

A decision support system supporting the proposed

approach has been developed by coupling GIS technology
and Rough set theory (Pawlak 1991).

The approach is validated using real-world data relative to
a nuclear risk management decision problem in the
southern France.

5

IN THIS PRESENTATION ….

1. Introduction of the proposed impact evaluation approach

2. Presentation of the case study with some conclusion

6

1. MULTICRITERIA IMPACT EVALUATION
APPROACH

Phase 1. Identifying the
stakes involved

Phase 2: Identifying

representative criteria

Phase 3: Quantifying
criteria scores

Phase 4: Group 7
multicriteria classification

APPROACH

Identification of the stakes involved including everything that can
be affected by an accident such as zones that are densely
inhabited, business activities, and cultural and environmental
assets.
Then one or more adverse effects have to be linked to each stake
so that they represent the consequences of an accident in various
sectors. 8

APPROACH

Once the various factors and adverse effects have been selected, the
criteria that characterize them have to be identified.
Formally, a criterion is a function qj, defined on a set of decision objects U
(which are districts in our case), taking its values in an ordered set, and
structuring the stakeholder's preferences according to some points of view.
The evaluation of an object u in respect to criterion qj is denoted qj(u).
We denote by Q={q1, …,qm} the set of m evaluation criteria. 9

APPROACH

This involves evaluating the consequences on each district in respect to
each criterion.
The output of this phase is an evaluation matrix where rows represent the
districts and columns represent the evaluation criteria.
Each box then contains the corresponding value of the criterion for the
district in question. In terms of this phase, each district u will be
associated with the vector (q1(u),…,qm(u)) which represents the
evaluations of u with respect to the criteria in Q. 10

APPROACH

The aim of group multicriteria classification phase is to assign the different
districts of the study area to different risk classes while taking into account
the perspectives of multiple stakeholders.
A multicriteria classification model, called Dominance-based Rough Set
Approach (DRSA) (Greco et al., 2002) an extension to rough sets theory
(Pawlak 1991) to multicriteria classification, will individually be used by the
different stakeholders.
Some appropriate aggregation rules are then used to coherently combine the
outputs of different stakeholders. 11
The DRSA is then used once again to obtain the final classification in terms
of vulnerability/risk levels of the districts.

4. CASE STUDY: NUCLEAR RISK MANAGEMENT
DECISION PROBLEM

The problem considered here concerns the management of post-
accident nuclear risk in the southern France region.

This problem has been conducted during the PRIME project,

which is supervised by the French Institute for Radioprotection
and Nuclear Safety.

A full description of the project is available in Mercat-Rommens et
al. (2010).

The study zone covers a radius of some fifty kilometers around
three nuclear sites in the lower Rhône Valley (the Cruas,
Tricastin-Pierrelatte and Marcoule sites). 12

DECISION PROBLEM

The objective of the PRIME is to develop, conjointly with the
experts, the stakeholders and representatives of the territory, a

multicriteria evaluation approach permitting to analysis and
characterize the contaminated territory that will be useful for the
managers of the risk.

Practically, the evaluation approach should associate to each
district of the study area a degree representing the risk on this
district of a nuclear accident resulting in releases into the
atmosphere.

For this purpose, a scale of six from 0 (for a situation described as
normal) to 5 (in the event of a major and long-lasting negative
impact) has been adopted by PRIME working team. 13

DECISION PROBLEM

The table describes the vulnerability measurement scale

14

4. CASE STUDY: APPLICATION. PHASE 1.
IDENTIFYNG THE STAKES INVOLVED

The stakes are organized into 3 groups:

(i) radioecological consequences which are related to the
contamination of urban, agricultural, costal and natural and

forest areas; Rhône River and ground water;

(ii) economic consequences related to contamination and damage on
companies, tourism activity, real estate and employment;

(iii) population reactions.

15

CHOOSING REPRESENTATIVE CRITERIA
Based on the stakes identified in the previous phase, a
comprehensive list of criteria has been identified by the different
stakeholders (see Mercat-Rommens et al., 2010).

For the purpose of the present paper, only a subset of criteria, will
be used for illustration.

16

QUANTIFYING THE CRITERIA SCORES

The quantification of criteria required the federation of available
radio-ecological data (field data, modeling, experimental results), as
well as territory data.

The assessment method of the radiological sensitivity indicators
invoked classic impact calculation models for radionuclides used at

the IRSN: CASTEAUR code for river discharges (see Duchesne et
al., 2003), ASTRAL code for forest ecosystem and food chain
contamination following accidental radioactive pollution (see
Renaud et al., 1999; Calmon and Mourlon, 2005), integrating the
spatial variability of parameters.

17

An extract of the obtained evaluation matrix that represents a common
information table for all the involved stakeholders for the 4 phase.

18 districts (x1,…..x18)
have been carefully
selected (from 491

districts) by PRIME
working team: these
districts are chosen to
be as representative as
possible by including
urban, industrial as
well as rural districts.

18

4. CASE STUDY: APPLICATION. PHASE
4. GROUP MULTICRITERIA CLASSIFICATION

Individual classification
Given the evaluation of the 18 selected districts in respect to all criteria, each

stakeholder is called to classify each of them on the global vulnerability scale.
The responses of the stakeholders are then used to define the values of the
decision attributes E1, E2 and E3 associated with the 3 stakeholders considered

in this paper.
Then each stakeholder should apply the DRSA on each decision table to get its
own classification.

Aggregation of the individual classification

Final classification

19


20

4. CASE STUDY: APPLICATION, 4. GROUP
MULTICRITERIA CLASSIFICATION
Aggregation
In this step, we first apply the aggregation procedure to
construct a common decision table with common Condition

attributes (Criteria) and Decision Attributes.

Final classification
Next, the Dominance-based Rough Set Approach (DRSA) is applied
to the common decision table to classify the districts of the
study area.

21

4. CASE STUDY: APPLICATION, 4. GROUP
MULTICRITERIA CLASSIFICATION
The result of
classification is shown
in Figure 1. The left-
hand side of the
interface shows the

global vulnerability
scale with shaded tones.
The map on the right-
hand side of the
interface shows the final
classification of the

different districts.
It is easy to see that
vulnerability decreases
relatively concentrically
around the Tricastin-
Pierrelatte nuclear site,
which is the location of
the fictive accident
considered in this case
study.

The obtained risk map represents the main decision support that could be 22
used by risk managers to effectively and rapidly manage the contaminated
districts by appropriately identifying the required measures for affected
districts

Evaluating Post-Accident Nuclear Risk Using GIS and Rough Set Theory

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