detection and ranking of vulnerable areas to urban flooding using gis and asmc (spatial analysis multicriteria) a case study in dakar, senegal

International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-2, Issue-8, Aug- 2016]
Infogain Publication (Infogainpublication.com) ISSN : 2454-1311
www.ijaems.com Page | 1270
Detection and Ranking of Vulnerable Areas to
Urban Flooding Using GIS and ASMC (Spatial
Analysis multicriteria): A Case Study in Dakar,
Senegal
M. L. Ndiaye1
, V. B. Traore2
, M. A. Toure3
, A. Sambou4
, A. T. Diaw1
, A. C. Beye2,4
1
Training and Research Laboratory in Geoinformation,Cheikh Anta Diop University, Dakar, Senegal
2
Laboratory of Hydraulics and Fluid Mechanics, Cheikh Anta Diop University, Dakar, Senegal
3
Laboratory of Climatology and Environmental studies, Cheikh Anta Diop University, Dakar, Senegal
4
Laboratory of Physics Solid and Sciences of Materials, Cheikh Anta Diop University, Dakar, Senegal
Abstract—Dakar region is confronted in recent years with
episodes of repetitive and devastating floods. The
structures in charge of the matter, yet does not have
enough knowledge of space and tools to preciselylocate
vulnerable areas. It is in this particular context that we
have through this study process by coupling GIS and
ASMC techniques. We aim in this coupling, to develop an
efficient tool for support decision making in terms of
identification strategies, intervention or adaptation. The
defined criteria for this are: the rainfall, the groundwater
level, geology, topography, wetlands, population density,
living standards and the type of habitat. We have used an
analytical hierarchical process (AHP) by foursteps: a) the
breakdown of problematic vulnerabilities; b) scanning and
harmonization of layers factors (criteria); c) the weight
assignment of different layers criteria according to the
comparison procedure in pairs; d) and aggregation
criteria layers, through the weighted superposition of the
SOC software tool. This has helped us to hierarchically
locate the vulnerable are as to flooding. The results have
showed a very low vulnerability (1.67%), low vulnerability
(50.53%), high vulnerability (43.66%) and a very high
vulnerability (4.14%). Approximately, 50% of the Dakar
region are vulnerable to flooding and particularly the
suburban area concerning the departments of Pikine and
Guediawaye. These informations are very useful for
governments in the effective and sustainable flood
management and identification of priority intervention
areas.
Keywords—GIS, ASMC, Vulnerability, urban flooding,
Dakar, Senegal
I. INTRODUCTION
The vulnerability of cities to risks and disasters has been a
growing academic interest [1]. The relative risk of urban
flooding is becoming more topical due to the exponential
growth of urban centers, population growth and climate
change. Flooding is one of the major environmental crises
one has to contend of within the century [2]. In all
countries, they become a scourge increasingly feared [3-4].
The vulnerability is perceived as a conjunction of risks,
impacts and adaptive capacity [5]. It is a form of insecurity
in the well-being of individuals, households and
communities including sensitivity to change [6-7]. "She's
the extent of harmful consequences of the flooding on the
issues" or "the fragility of a socio-economic system as a
whole with risk" [8]. It is a lack of resilience to
environmental change, economic and social that threaten
the well-being. Vulnerability to flooding is inherently
linked to the place, that is to say the physical configuration
and man in the middle: some areas are more vulnerable
than others. The two other urban vulnerabilities are
intrinsic. There is first the destructive effects of natural
phenomena, which are compounded by the very structure
of materials and urban morphology. Urban morphology
accentuates the induced effects, since the site early cities
was quickly overwhelmed by the consumption of the
available space, or exposed to the dangerous sites. The
second factor is the pronounced segmentation of urban
society, reflecting the underdevelopment and socio-
economic conflicts in a limited and coveted space [9]. In
West Africa, urban populations are increasingly vulnerable
to flooding. The vulnerability of the Dakar region is
mainly due to physical, economic, environmental, which
reduce people's adaptive power faces the floods that are
more exposed. Indeed, the landscape of the region is
marked by the alternation of dunes and dune slacks called
inter niayes[10]. The regions and the most vulnerable
societies to flooding and climate change and their damage
are those whose economic situation is unfavorable and

whose location is at risk [11-12-13]. In the periphery of the
Dakar region, the repetition of these phenomena pushes
people to adopt multiple responses to the risk and its
consequences [14]. Since 2005 Dakar is increasingly
confronted with repeated flooding, especially in the peri-
urban area, corresponding to the departments of Pikine and
Guediawaye. In 2009 it was about 360 000 people who
were directly affected by floods in Pikine and 22 000
people Guediawaye; respectively 44% and 7.2% of the
population in both cities [15]. The number of concession
affected by the floods is estimated at 20 000 in these two
departments [16]. Apart from habitat degradation, floods
cause considerable economic losses for the population and
lead to degradation of social ties within these urban areas.
Also part of life (housing, equipment and infrastructure) is
highly degraded [14-17].Dakar capital of Senegalis
characterized by an out of control urbanization process
[18-19-20-21-22-10]. The reasons of this are the rapid
growth of population and settlements over time while a
limited effort is made to better manage urban areas
particularly in the outskirts of Dakar [20]. Despite efforts
by actors in the field of research palliation floods,
populations of the periphery of the Dakar region still live
in fear of storms that each year seem to be growing in the
'particular ecosystem Niayes where habitats are. It is
indeed unfortunately not possible to respond to flooding
problems only through the implementation of retention
basins or granting of pumps and foods; today we have an
obligation to think about legal solutions, structural,
organizational and preventive [23]. This national policy
did not solve all the problems which local actors face. This
calls into question the relevance and effectiveness of
prevention and fight against floods. Consequently, it is
essential to develop a comprehensive program that
highlights a transversal approach both in terms of actors
that point of view the proposed solutions. The feedback
and expert missions insist that within ten years, the
consideration of risk must necessarily pass through the
revaluation of vulnerability studies as the basis necessary
for setting goals for management regionalised risk [24-25-
26]. Several work on the assessment of vulnerability to
flooding in the region are based on a quantitative approach
and focus on specific areas. Efforts, however, remain to be
deployed on studies based on modeling across the region
through advanced hierarchical techniques, given the mass
of heterogeneous information to take into account. Today
the Advanced techniques have revolutionized their
mechanisms of decision making and are used in a large
number of area agriculture for example [27-28-29-30-31].
It is therefore necessary to develop tools to aid the decision
on vulnerability knowledge locally for risk prevention.
Vulnerability to flooding depends, among other factors of
importance and frequency of flood factors such as rain,
soil type, geology, topography, hydrology, land use,
structuring housing, population density. This research
proposes to use the operational and scalable tools to aid in
evaluation and decision in management of urban flooding.
The assessment of urban vulnerability is based on the
fullest possible identification of issues of territory and a
method for decision support, based on expert judgment,
allows to assess their vulnerability. The results are
transcribed in the form of maps, through GIS, using geo-
processing operations. This is to develop analysis and
evaluation grids enabling decision makers to increase their
knowledge of local vulnerabilities. These vulnerability
assessment grids are implemented in order to meet a
specific demand facing the risks of urban flooding.
II. STUDY AREA AND DATA
The region of Dakar is located in the extreme west of
Senegal between longitude 17 ° 10 'and 17° 32'W and
latitude 14° 53' and 14° 35 'N (Fig1.). It covers an area of
550 km², representing 0.28% of the total area of the
country. Administratively, the region of Dakar is divided
into four departments (Dakar, Guediawaye, Pikine, and
Rufisque) and 10 districts. It houses 53 local authorities: a
region, 6 towns, 43 district municipalities and 2 rural
communities. It is bounded to the east by the Thies region
and the north, west and south by the Atlantic Ocean [32].
It has several distinctive physically. Geomorphology is
characterized by a dune terrain, topography is low [33],
geology leaves appear several formations such as dune
sand, clay and organic sandy clays. It is characterized by a
coastal type of microclimate due to its advanced position
in the Atlantic. This is strongly influenced by maritime
trade winds and the monsoon which respectively set from
November to June and from July to October in directions
N-NW and S-SE. It is characterized by two seasons, a
rainy season from June to October and a dry season from
November to May.The average annual rainfall is estimated
at 400mm [34]. The minimum temperatures range (12° to
20° C), maximum temperatures range from (28° C to 36°
C). The average temperature is between (20° C and 28° C).
The data used in this study are of map and satellite natures.
These include mapping a geological map of the Dakar
region 1/50000 [35], a map of the deep web as points [36],
a map of the inequality of wealth [37] and a population
density map [38-21]. For remote sensing satellite data, we
have a Landsat image of 2014 (OLI) for the extraction of
wetlands, a TRMM and SRTM image, for the extraction of
rainfall and DEM data.

Fig.1: Location of the study area
III. MATERIALS AND METHODS
3.1. Data processing
Map data is initially georeferenced and projected into the
system WGS 1984 UTM Zone 28 N, before being
converted to raster mode. The pixel size of the images is
homogenized at 30 m. We also use the interpolation
method, the IDW method for spatial point data of the
depth related to the web for their spatialization to
compare to the rest of the database. For remote sensing
data, we have georeferenced the TRMM images and then
converted into ASCI format before proceeding with their
interpolation. We then extract wetlands (lakes and flood
zones) from the Landsat image classification of 2014.
Euclidean distances are performed on these areas to
determine the different levels of vulnerability. Regarding
the DTM, we first defined a projection him prior to the
correction of missing pixels from the Arc Hydro tool.
Thereafter we have calculated the TIN that have been
reclassified to get the DEM of the Dakar region.
3.2. Multi-criteria analysis
Multi-criteria analysis allows a choice between several
solutions by breaking an analysis grid in several criteria,
each weighted coefficient or a relative weight. A criterion
is a function defined on all shares representing the
preferences of the user according to his point of view; in
our case they concern the most vulnerable to flooding
factors. We conducted an identification of criteria that
will be based analysis and their assign weights according
to their relative importance. These weights are obtained
by the pair comparison approach defined by [39-40-41].
3.2.1. Criteria identification
A criterion is a judgment factor based on which measured
and evaluated an action [42-43]. The criteria are of two
types, constraints and factors. Constraints allow to single
out vulnerable areas deemed those are not factors as are
criteria that define a certain level of fitness or alternative
solutions for all regions [44]. Based on the data available
to address the problem of flooding in Dakar, we have
identified eight criteria that allude to "factors" layers.
These are: topography, water table level, population
density, housing type, wetlands, wealth inequality,
rainfall and geology of the region.
3.2.2. Standardization of criteria
The standardization is to harmonize different criteria
layers involved in the analysis system. We performed the
treatment in three steps: (i) all layers were reduced to
raster format with the same number of row and column.
To this, we used a boundary layer in the region in 2014,
which served to extract all the other information on the
mapping; (ii) the pixel size is 30 m resolution; (iii) all
criteria layers were reclassified into 5 sub-criteria
according to their level of vulnerability. After this
normalization, we got 8 layers criteria (or factors) that
will be translated to a stage of weighting and aggregation
leading to the establishment of a decision-making board.
3.2.3. Weighting factors
The weight is a relative percentage, and sum of the
weights of influence percentages must equal 100. If the
number of factors is high, it is often difficult to estimate
the relative weight of each of them (Soto etRenard 2001;
Kêdowidé, 2010). One solution is to compare each factor
with the other, a paired comparison (Table 2) and then
deduct the total weight resulting from statistical

calculation. This method was adopted, given the number
of factors in play in the conduct of the CMA set at 8. The
assessment of flood vulnerability criteria based on the
judgment of 13 experts involved in the field of
management of urban and flooding issues such as
planners, managers, actors, local authorities, civil
engineering engineers , sanitation and surveyors, etc. The
objective here is to bring the maximum judgment of
actors and have a wide viewing angle on the vulnerability
of the site to flooding. Factor analysis by pair has
generated a weighting matrix and to perform the
calculation of the weights on the basis of these weights
(Table 3). A 1 to 5 rating scale is allocated to the diaper
criteria, in increments of 1 (1 is the lowest and 5 the
appropriate is more). Table 4 shows the 5 classes
identified for each criterion, starting from the low
vulnerability class to the very high vulnerability class. In
each class, we assign a weight in an arithmetic
progression system for more originality in the
classification.
Table .2: Scale of [39-40] for weighting factors in pairs
Table .3:Weighting factors in pairs according to the scale of Saaty with the weight of the resulting factors
3.3. Aggregation of the layer
Aggregation involves combining all the criteria layers to
obtain because of vulnerability of the region to urban
flooding. The weighted linear combination allows
complete aggregation and generates a vulnerability map,
on which each pixel is the weighted sum of all the criteria
taken into account. The value of the cells obtained is
added to give a result as raster. This is a decision support
tool in terms of assessment, intervention for better
management of the problem of flooding.
IV. RESULTS
The aggregation of various factors according to their level
of influence expressed by relative weight has helped
develop a vulnerability map with floods in the Dakar
region (Fig. 2). Table 2 shows the areas and the
corresponding percentages at each level of vulnerability.
The analysis of the map of vulnerability of the Dakar
region floods indicates a trend range and to assess the best
level of vulnerability of the region; we can distinguish
them in very high risk areas (4.14%), the high
vulnerability areas (40.36%), areas with low vulnerability
(50.33%) and very low vulnerability (1.67% ).

Fig. 2: Vulnerability to flooding in the Dakar region
Table.1. Vulnerability ranks for flooding in Dakar urban area
In the vulnerability map, the areas to the heart of the
Dakar region, specifically in the municipalities located in
the departments of Pikine and Guediawaye seem most
vulnerable to flooding (Figure 3 and Table 2). From
Figure 4, the very high vulnerability areas primarily
concern the department of Pikine at 91%, followed
Guediawaye to 5.88% and 3.12% in Dakar. The high
vulnerability relates against the four departments with
Rufisque respectively (58.04%), Pikine (24.88%), Dakar
(13.49%) and Guediawaye (3.59%). The low
vulnerability is recorded in the departments of Rufisque
(83.86%), Dakar (13.49%), Pikine (2.16%) and
Guediawaye (1.01%). The very low vulnerability to
flooding is recorded exclusively in the department of
Dakar close to 100%. This situation of high vulnerability
to high vulnerability and respect mainly Pikine and
Guediawaye is justified through the different factors used
in their identifications. Each individual factor shows a
significant vulnerability in these two geographical areas.
Lower altitudes, the lowest levels of water, high
population density, the lowest standard of living, lack of
rainwater drainage network,..., characterize these
municipalities in the region. This explains their relatively
high vulnerability compared to the rest (Dakar and
Rufisque).

Fig. 3:Proportion of vulnerability by department
Table 2:Vulnerability levelby department (km² and %)
Fig. 4:Vulnerability to flooding across the departments in the Dakar region (percent)
V. DISCUSSION OF RESULTS
The GIS-AMC coupling wants adamant in resolving
issues concerning urban flooding. In this study, we have
from this approach, spatial flooding and risks to Dakar to
locate the most vulnerable areas. This step is essential for
the authorities in measuring the priority intervention. The
vulnerability was illustrated by 05 levels (very low, low,
medium, high and very high). Approximately, 50% of the
Dakar region area (270km²) are vulnerable to flooding.
Analysis of the results reveals that the departments of
Pikine and Guediawaye, as the most vulnerable. This
shows that these departments are home to lower altitudes,
0.00 0.00
100.00
0.00
83.86
1.01
12.98
2.16
58.04
3.59
13.49
24.88
0.00
5.88
3.12
91.00
0
20
40
60
80
100
120
Rufisque Guédiawaye Dakar Pikine
Very Low Low High Very High

the lowest levels of water, high population density, lack
of development, the lowest standard of living, lack of
rainwater drainage network and sanitation... These
conclusions make available to the authorities and decision
makers an operational tool for managing urban floods in
14 regions of Senegal in general and particularly in the
Dakar region.
VI. CONCLUSION
Our motivation through this study is to provide basic
knowledge to decision makers through powerful tools in
the fight against floods. In this study, we have conducted
by identification of vulnerability factors, its processing,
evaluation and aggregation.The results reveal that about
50% of the Dakar region are vulnerable to flooding.
Analysis of results has showed that Pikine and
Guediawaye remain the most vulnerable areas to
flooding. This study has therefore showed the relevance
of GIS-MCSA coupling in the detailed characterization of
the areas at risk. It is an important decision support for
urban planning and environmental management.
REFERENCES
[1] Ologunirisa T. E., 2009. Strategies for Mitigation of
Flood Risk in the Niger Delta, Nigeria, J. Appl. Sci.
Environ. Manage. June, 2009 Vol. 13(2) 17- 22.
[2] Emmanuel Udo A., Ojinnaka O. C., Baywood C. N.,
Gift U. A., 2015. Flood Hazard Analysis and
Damage Assessment of 2012 Flood in Anambra State
Using GIS and Remote Sensing Approach, American
Journal of Geographic Information System 2015,
4(1): 38-51. DOI: 10.5923/j.ajgis.20150401.03
[3] Provitolo D., 2007. Vulnerability Mediterranean
flooding in urban areas: a new geographical
approach, 1/2007 Annals of geography (No. 653), p.
23-40.
[4] Nouaceur Z., 2015. The sahelian capitals facing the
resurgence of urban flooding, Territorium 22, 131-
140.
[5] IPCC, 2001a. Climate Change assessment (2001):
Impacts, Adaptation and Vulnerability. Report of
Working Group II of the IPCC, Geneva, Switzerland,
101 p.
[6] Moser C. and Satterthwaite D., 2008. Towards pro-
poor adaptation to climate change in the urban
centres of low and midldle-income countries, Climate
Change and Cities Discussion Paper 3, Theme:
Climate Change and Cities-3, Human Settlements
Discussion Paper Series, Global Urban Research
Centre, IIED, 45 p.
[7] Moser C., 2010. A conceptual and operational
framework for Pro-poor Asset Adaptation to Urban
Climate Change, Non published Paper
[8] Hubert G.,Ledoux B., 1999. The cost of risk ... The
evaluation of socio-economic impacts of floods,
Presses of the National School of Bridges and Roads,
Paris, 232 p.
[9] Thouret, JC.,Ercole A., 1996. Vulnerability to natural
hazards in urban areas: effects, factors and social
responses, Cah. Sci. hum. 32 (2) 96: 407-422.
[10]Diop, A., CNiang. I., MbowC., DialloA.D.,2014.
Study of the vulnerability of ThiaroyesurMer floods:
factors and effects, links New Series, No. 18
[11]IPCC, 2001b. Climate Chante Synthesis Report.
Genève: World Meteorological Organization, U.N.
Environment Programme, 397 p.
[12]IPCC, 2007. Contribution of Working Group II to the
Fourth Assessment Report of the Intergovernmental
Panel on Climate Change. Summary for
Policymakers, 12 p.
[13]PNUD (2008). To adapt to the inevitable: national
action and international cooperation. In World Report
on Human Development 2007/2008, Chapter 4, p 36.
[14]Diongue M. (2014). Urban periphery and risk of
flooding in Dakar (Senegal): Yeumbeul North, Eso,
work & materials, No. 37
[15]IAGU, 2014. Floods in the suburbs of Dakar:
Towards an adaptation by the improvements of
buildings, infrastructure and local governance to
reduce the vulnerability of the assets of households
and communities
[16]CSE, 2010. Report on the state of the environment in
Senegal, p. 265.
[17]Sane, O.D., Gaye, A.T., Diakhate, M. and
Aziadekey, M., 2015. Social Vulnerability
Assessment to Flood in Medina Gounass Dakar.
Journal of Geographic Information System, 7, 415-
429.
[18]Mbow L. S., 1992, Dakar: urban growth and
mobility, State Doctoral dissertation, University of
Paris X Nanterre, in October 1992, 2 Volumes, 709
p.
[19]Wade, S., Faye S., Dieng M., Kaba N.R. Kane M.,
2009. Remote sensing of urban flood disasters: the
case of the region of Dakar (Senegal), Scientific
Animation Days of AUF Algiers in , 2009
[20]Mbow C., Diop A., Diaw A. T., Niang C. I., 2008.
Urban sprawl development and Flooding at
Yeumbeul Suburb (Dakar, Senegal), African Journal
of Environmental Science and Technology, Vol. (4),
pp. 75-88.
[21]ACS, 2012. Urban Development Strategy of the great
Dakar (2025), Cities Alliance, UN-HABITAT,
Senegal, 74 p.
[22]Ndao, M., 2012. Environmental dynamics and
management from 1970 to 2010 wetlands in Senegal:

study of land use remote sensing Niayes with
DjiddahThiaroye Kao (Dakar) Mboro (Thiès and
Saint Louis), 3rd cycle thesis, 365 p.
[23]Djigo, 2009. Urban flooding in Senegal are becoming
more dangerous and less manageable Article in
Dakar Info, visited the site 31-12-2014
[24]Huet P., Martin X., Prime J.L., Laurin H.P., Laurain
Cl., Cannard Ph.,2003. Back in September 2002
flood experience in the Gard, Hérault, Vaucluse,
Bouches du Rhone, Ardeche and Drome. IGE
(General Inspectorate of the Environment), 133P.
More Annexes A and B.
[25]D. Soto, F., 2012. Space Assessment of flood risk by
crossing the hazard and vulnerability issues:
application to flooding of Greater Lyon, 10 pages
[26]Barroca B., PottierN., LefortE.,2005. Analysis and
evaluation of vulnerability to flooding of downstream
barley pool, Seventh Theo Quant Meetings, January
2005, 12 pages.
[27]Ahamed T.R, GopalRao K, Murthy J.S.R, 2000. GIS-
based fuzzy membership model for crop-land
suitability analysis. AgrSyst 63(2):75–95.
[28]Prakash T.N., 2003. Land Suitability Analysis for
Agricultural Crops A Fuzzy Multicriteria Decision
Making Approach. The Netherlands: International
Institute for Geo-information Science and Earth
observation Enschede;. pp. 6–13. (2).
[29]Samanta, S., Pal, B., Kumar, D., 2011. Land
Suitability Analysis for Rice Cultivation Based on
Multi-Criteria Decision Approach through GIS Int. J
Sci. Emerging Tech. Vol-2 No. 1 October
[30]Kedowide, C.G. (2010). GIS modeling assessment by
multi-criteria for the exploration of sites of "urban
agriculture, Ouagadougou. Vertigo, V.10, N2.
[31]Ayehu G. T., Besufekad S. A., 2015. Land Suitability
Analysis for Rice Production: A GIS Based Multi-
Criteria Decision Approach, American Journal of
Geographic Information System 2015, 4(3): 95-104.
DOI: 10.5923/j.ajgis.20150403.02
[32]ANSD, 2013, A monographic study on real estate
husing in Dakar (EMSILD), Economic Statistics
Department and National Accounts, 95 p.
[33]Dasylva, S., Cosandey C., 2006.The exploitation of
groundwater Quaternary sands for drinking water
supply of Dakar offer compromised by inadequate
rainfall recharge.Géocarrefour. 80 (4), pp. 349-358
[34]Diop M., Sagna P., 2012. Vulnérabilitéclimatique des
quartiers de Dakar au Sénégal :Exemple de Nord-
Foire-Azur et de Hann-Maristes, Renforcer la
resilience au changementclimatique des villes : Du
diagnostic spatialisé aux mesuresd’adaptations
(2R2CV), 7 et 8 juillet 2011, Université Paul
Verlaine – Mertz, France, Actes du Colloque, 12 p.
[35]Michel, P., SallM. M.,1984. Atlas of Senegal, A. J.
Paris Edition, p. 74
[36]UNEP/UNESCO/UN-HABITAT, 2003.Shallow
aquifer and urban pollution in West Africa, Senegal
Progress Report, 39 pages.
[37]Borderon, M., S. Oliveau, V. Machault, C. Vignolles,
J.P. LacauxNdonky and A., 2014. Qualify urban
spaces in Dakar, Senegal: Preliminary results of the
approach cross between remote sensing and spatial
census data, European Journal of Geography
[38]Lombard J., 2006.Private issues in public transport in
Abidjan and Dakar Géocarrefour, Vol. 81/2 | 2006,
167-174.
[39]Saaty, T. L., 1977. A method for scaling Priorities in
hierarchical structures, Journal of Mathematical
Psychology.15, pp.234-281.
[40]Saaty T. L., 1980. The Analytic Hierarchy Process.
New York (USA): McGraw-Hill, 287 p.
[41]CIRAD and CIFOR, 2000. Application of multi-
criteria analysis in the evaluation of criteria and
indicators, Manuals criteria and indicators for
sustainable forest management, 74 p.
[42]Chakhar S., Martel J.-M., 2003, Enhancing
geographical information
systemscapabilitieswithmulticriteria evaluation
functions. Journal of Geographic Information and
DecisionAnalysis, Vol. 7, No. 2, pp. 47-71.
[43]Balzarini R., 2013.Cognitive approach for an
integrating the geomatics tools in environmental
science: Modeling and Assessment, PhD University
of Grenoble, 327 pages.
[44]Godard, V., 2005. GIS and Decision Support, course
notes, University of Paris 8, France.

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