Ionela Craciun 4 [627279]

4.3.2.5. Validation and c lassification results
In order to assess the performance of the algorithm, the overall accuracy was calculated.
The validation of performed for 10 cities outside outside Romania, for which the Urban Atlas data
were available (Bratislava, Budapest, Debrecen, Kosice, Presov, Miskolc, Plovdiv, Sofia, Szeged
and Varna). The overall accuracy was calculated by dividing the number of the correct
classifications to the total number of segments. The results showed a good accuracy of th e
algorithm, the accuracy ranging from 0.68 to 0.83 ( Annex 4 ). An example of the input data used
for validation and output data of the algorithm is presented in Fig. 32.

Fig. 32. Input and output data for the validation of the algorithm.
Sofia city, Bulg aria (accuracy 0.76)

The land use classes were determined for the area of i nterest – the flood prone areas . In
this way, seven land use classes were obtained: continuous urban, discontinuous urban, industrial,
infrastructure, agricultural, forests and wat er (Fig. 33 ).

Fig. 33 . Land use map developed for the flood prone area using
Landsat 8 satellite imagery

4.3.3. Damage analysis
In order to calculate the possible direct tangible damages at national level, the free and
open source FloodRiks plugin was used. This plugin calculates the economic damages and the loss
of lives and can be used for different scales analysis allowing to the users to apply different depth –
damage functions available in its repository or to enter new functions. In this study the J RC depth –
damage functions were used as well as the maximum damage values provided by JRC (Huizinga
et al., 2017). The main input data that were used for this study are:
– hazard data containing the water depth ;
– exposure data that are represented by the l and u se map that contain information regarding the
location of exposed elements and the maximum damage value of this elements.
The damages were calculated for four sets of input data:
– JRC water depth and Corine Land Cover (CLC)
– GFI water depth and Landsat 8 land use

– JRC water depth and Landsat 8 land use
– GFI water depth and CLC
The JRC water depth is the pan -European hazard map developed by Alfieri et al., 2014;
the GFI water depth represents the hazard map developed in this study using the GFI methodology;
the Landsat 8 land use represents the land use map developed in this study using Landsat 8 satellite
images.
The JRC depth -damage functions provided by the FloodRisk plugin are associated to the
land use classes of Urban Atlas database. In order to have a correct association between this
functions and the land use classes used in this analysis, a set of reclassification must be done. First,
to each of the CLC class a corresponding Urban Atlas code is associated. The land use map
obtained using the Landsat 8 data ( Landsat 8 land use map ) contains seven classes (continuous
urban, discontinuous urban, industrial, infrastructure, agricultural, forests and water). Therefore,
in order to have consistent comparable results regarding the damages of the four conside red
scenarios, the Urban Atlas codes associated to the CLC classes were further grouped in seven
classes ( Table 14 ). The description of the CLC and Urban Atlas codes is presented in Table 3 and
Table 4 . This codes were further associated to the correspondi ng classes of Landsat 8 land use map
(Table 15 ).

Table 14 . Reclassification of CORINE land use classes
Corine code Urban Atlas
code Urban Atlas
reclassification
code Description of Urban Atlas codes
111 11100 11100 Continuous Urban Fabric (S.L. > 80%)
112 11220 11220 Discontinuous Medium Density Urban Fabric (S.L. :
30% – 50%)
121 12100 12100 Industrial, commercial, public, military and private
units 131 13100
132 13100
133 13300
122 12220 12220 Other roads and associated land/ Infrastructure
123 12300
124 12400
141 14100 20000 Agricultural + Semi -natural areas + Wetlands

142 14200
211 20000
212
213
221
222
231
242
243
321
322 13400
331
332
333
411
412
311 30000 30000 Forests
312
313
324
511 50000 50000 Water bodies
512

Table 15 . Correspondence between Landsat 8 land use classes and Urban Atlas codes
Description Urban Atlas code
Continuous urban 11100
Discontinuous urban 11220
Industrial 12100
Infrastructure 12220
Agricultural 20000
Forest 30000
Water 50000

For each land use class a value was determined, representing the asset value. For this
purpose the data provided by Huizinga et al., 2017 were used. In this study a consistent set of
maximum damage values was developed for different countries and for various damage classes
(Huizinga et al., 2017) This data are representative of the year 2010, therefore in order for the data
to be accurate for the year 2017, the inflation was calculated and the data were adjusted ( Table
16). During the analysis it was observed that the continuous urban class of CLC is covering small
areas, not being present in the flood prone areas, therefore the same assets value was associated to
both urban classes. Th e CLC does not have an accurate distinction between continuous and
discontinuous urban, most of the urban areas being considered discontinuous. On the other hand
the Landsat 8 land use map differentiates the two classes for all the urban areas. Therefore, it is
not possible to used different values for the two classes, because in the case of CLC map the
damages for urban will be calculated with the smaller value (the one of discontinuous class) while
in the case of Landsat 8 map the damages will be calculat ed with different values for the two
classes.
Table 16 . Assets value for the study area
Code Land use class Assets value (euro/m2) Adjusted assets value (euro/m2)
11100 Continuous urban 417 495
11220 Discontinuous urban 417 495
12100 Industrial 561 667
12220 Infrastructure 9.44 11.2
20000 Agricultural 0.06 0.07
30000 Forest 0.04 0.04
50000 Water 0 0

4.4. Results and discussions
For the first scenario of flood damage assessment, the next data were used: the water depth
map provided by JRC ( JRC water depth ) and the Corine land Cover (CLC) database from 2012.
The JRC water depth map represents the flood events with 100 years return period for the entire
Europe and has a resolution of 100m (Alfieri et al., 2014). As demonstrated by Alfieri et al., 2014,
the water depth map showed a good performance, it can be used for analysis at large scales. The
data provided by CLC are available at a scale of 1:100000 and include 44 land use classes. For the

second scenario the hazard data were represented by the national water depth map with a resolution
of 30 m developed in this study ( GFI water depth ). For the exposure data, the map developed from
the Landsat 8 images with a resolution of 30 m ( Landsat 8 land use ) was used. The results are
presented in Table 17 and Fig. 34 .
Table 17 . Damage results for the next scenarios: a. JRC water -depth and CLC;
b. GFI water -depth and Landsat 8 landuse
Land use class JRC – CLC damages (Meuro) GFI – Landsat 8 damages
(Meuro)
Urban 101155.47 283452.07
Industrial 37718.22 445927.42
Infrastructure 120.74 3300.41
Agricultural 749.87 214.02
Forests 21.56 25.76

Fig. 34 . Graphic representation of damage results for the next scenarios:
a. JRC water -depth and Corine land use; b. GFI water -depth and Landsat 8 landuse

For the first scenario (JRC – CLC) the results showed that 72% of the total damages are
represented by the damages from urban areas, this sector being the most affected, while the
damages in the industrial areas represent 27% of the total damages. For the second scenario (GFI
– Landsat8) the greatest damages are represented by the industrial areas with a percentage of 61%,
while the damages in the urban areas represent 39% of the total damages.
The second scenario shows an overestimation of the damages, pa rticularly in the urban and
industrial areas. However, this can be explained by the use of a more detailed flood map that was
developed in this study, using the GFI method. This method provided flood maps for all the rivers,
72%
27%
0%
1%
0%JRC_CLC
Urban
Industrial
Roads
Agricultural
Forests
39%
61%
0%
0%
0%GFI_Landsat 8
Urban
Industrial
Roads
Agricultural
Forests

including the small stream, whi ch are not considered in the flood analysis maps of JRC, therefore
in the case of GFI flood map more areas are affected by floods and in conclusion greatest damages
are reported.
Furthermore, in the second scenario the damage value registered for industri al area is
higher than the urban damage value. This fact can have two causes: (1) the GFI water depth map
is affecting a larger industrial area compared with the JRC map; (2) the Landsat 8 land use map is
overestimating the areas covered by industrial, com paring with CLC map.
In order to determine which of the input data is causing the greatest differences in the
results, another two scenarios were simulated: GFI and CLC; JRC and Landsat 8. The results are
presented in Table 18 and Fig. 35 .
Table 18 . Damage results for the next scenarios: c. GFI water -depth and Corine land use;
d. JRC water -depth and Landsat 8 landuse
Land use class GFI – CLC damages
(Meuro) JRC – Landsat 8 damages
(Meuro)
Urban 242954.451 74826.2565
Industrial 67540.5823 196963.496
Infrastructure 141.953 3546.7985
Agricultural 800.126 703.0057
Forests 31.1435 30.6149

Fig. 35 . Graphic representation of damage results for the next scenarios:
c. GFI water -depth and Corine land use; d. JRC water -depth and Landsat 8 landuse

In Fig. 36 , a comparison of the damage values calculated for the four scenario s is presented.
78%
22%
0%
0%
0%GFI_CLC
Urban
Industrial
Roads
Agricultural
Forests
27%
72%
1%
0%
0%JRC_Landsat 8
Urban
Industrial
Roads
Agricultural
Forests

Fig. 36 . Damage value comparison between the f our scenarios.

The results showed that when keeping a constant land use and apply different hazard maps
(GFI and JRC ), there are no significant differences in the percentage of damage values. For instant,
when using the Landsat 8 land use together with JRC and then with GFI, a 9% difference is
observed between the 2 scenarios for industrial areas. For the CLC simulation s with JRC and GFI,
the difference is 5%. This results indicate that the choice of hazard map does not have a great
influence on the damage results. Moreover , the simulations done with CLC show a larger percent
of damages in the urban areas, while the simu lations done with Landsat 8 have a larger percent of
damages in inductrial areas.
On the other hand, when a constant hazard map is used with different land use maps (CLC
and Landsat 8) a great difference in the results can be observed. For example, when u sing the JRC
hazard map with CLC land use map and then with Landsat 8 land use map, the difference in
percentage of industrial damages is 45%. For the simulations with GFI hazard map and CLC land
use and then with the Landsat8 land use map, the difference between industrial damages is 39%.
For both cases when using the Landsat 8 map (Landsat 8 -GFI; Landsat 8 -JRC) the industrial
damages are greater than the urban ones. This fact indicate that the Landsat 8 land use have a
greater influence on the industrial damage results, overestimating them.
Regarding the urban damages, the results showed a good accuracy between scenarios.
When comparing same hazard map with different land use the results are similar; the difference

that can be observed in the urban damage s is the fact that the scenarios using the GFI maps have
overestimated results; however this situation was previously explained by the extended flood area
of GFI map (Fig. 37 ).

Fig. 37. Comparison between urban damages
Regarding the industrial areas, t he use of CLC (in two scenarios) shows similar results,
with a small overestimation when it is combined with GFI hazard map. However, the use of
Landsat 8 land use show an overestimation in both cases, the use of GFI hazard map increasing
even more the dam age value ( Fig. 38).

Fig.38. Comparison between industrial damages

The differences caused by the land use maps may be given by the resolution of the data.
The CLC has a resolution of 100 m, offering a coarse classification of the land use. The Landsat 8
land use map has a resolution of 30 m, offering therefore more accura te results. When the maps
are analyzes by simple visualization, it can be observed that the Landsat 8 contains a larger
industrial areas compared to CLC, this fact being a result of the higher resolution of Landsat 8 land
use map.
In order to prove this h ypothesis, that larger industrial areas are identified when the
resolution of land use data is higher, the classification of CLC (100 m resolution) was compared
with the classification of Urban Atlas (10 m resolution). A confusion matrix was apply for all areas
in Romania where Urban Atlas data were available (35 cities). The results showed a general good
accuracy, however when just the industrial class is considered, the results showed a low accuracy
(Table 19 ). This demonstrates that the CLC fails to corr ectly identify the industrial area and
therefore the damages resulted using this land use map may present large uncertainties.
Table 19. Results of confusion matrix between CLC and Urban Atlas
The accuracy between Corine land cover and Urban Atlas land use
Analyzed cities Total accuracy Urban accuracy Industrial accuracy
Alba Iulia 0.86 0.79 0.29
Arad 0.90 0.87 0.39
Bacău 0.81 0.89 0.44
Baia Mare 0.87 0.78 0.61
Bârlad 0.91 0.92 0.43
Bistrița 0.85 0.60 0.44
Botoșani 0.90 0.76 0.54
Brăila 0.84 0.89 0.51
Brașov 0.90 0.77 0.53
București 0.81 0.87 0.62
Buzău 0.85 0.79 0.43
Călărași 0.91 0.87 0.72
Cluj-Napoca 0.81 0.78 0.52
Constanța 0.81 0.87 0.62
Craiova 0.83 0.80 0.62
Drobeta -Turnu Severin 0.85 0.78 0.60
Focșani 0.88 0.83 0.30
Galați 0.90 0.87 0.63
Giurgiu 0.80 0.82 0.68
Iași 0.84 0.78 0.48
Oradea 0.77 0.76 0.50

Piatra Neamț 0.87 0.84 0.66
Pitești 0.79 0.83 0.51
Ploiești 0.85 0.78 0.64
Rîmnicu Vâlcea 0.81 0.76 0.67
Roman 0.88 0.89 0.57
Satu Mare 0.91 0.81 0.43
Sibiu 0.88 0.83 0.32
Slatina 0.80 0.83 0.68
Suceava 0.85 0.79 0.46
Târgoviște 0.80 0.77 0.52
Târgu Mureș 0.80 0.79 0.41
Timișoara 0.80 0.87 0.46
Tulcea 0.75 0.84 0.63

It is acknowledge the fact that the analysis at large scales have the tendency of
overestimating the damages due to the fact that the data are aggregated and the values of the assets
are generalized and uniform. This gap may be solved by improving the resolution of the data that
are used (such as the DEM, water depth, land use) at large scales. The u se of local detailed datasets
for the assets value may also improve the accuracy of the results, however this data are not
harmonized for large areas and they may not be available. In fact, the free availability of data
represent an important problem in th is research field inducing great uncertainties in the process of
flood damage assessment.
In this study a methodology for flood damage assessment at national scale was proposed,
using free and open source data and tools with the aim of providing an improve d and simplified
method of flood damage assessment in data -scarce environments . The advantage of this
methodology is the fact that it can be applied over large areas with limited availability of data. The
study proposed innovating and up to date methods fo r all three steps of flood damage assessment:
hazard, exposure and vulnerability analysis.
Furthermore, the resolution of the data (water depth and land use) used in the second
scenario may improve the accuracy of the results, showing that constructed area s may be more
susceptible to floods and therefore more attention must be given to the urban planning, flood risk
management and risk reduction measures.

4.5. Conclusions
In this paper, a large -scale flood damage assessment methodology for data -scarce
environments is proposed combining EO high -resolution data (30m resolution) and free and open –
source GIS tools. The main goal of this study is to offer a cost -effective and parsimonious approach
that can provide solutions for the current principal gaps in large -scale flood risk assessment and
could support different stakeholders in their compliance with risk map delineation and the
management of current and future flood risk.
Compared with the current approaches in the field, the proposed methodology have some
advantages. First, a GIS -base model that is using the morphology of the basins in order to calculate
the water depth is applied. This method can be applied in data -scarce environments and it consider
in the analysis all the streams of the basins, the existing large -scale hazard maps considering just
the main rivers. Second, high -resolution data (30 m resolution) were used for both hazard and
exposure analysis, while the current analysis in the field are ranging from 100 m to 1 km. Finally,
a simple and flexible GIS tool (FloodRisk) was used in order to quantitatively estimate the
damages. Furthermore, the pan -European harmonized damage functions (JRC depth -damage
functions) were applied. This tool can be used for different scale applications, allowing a ny type
of input data (concerning the level of detail and availability), being a simple alternative for the
complex damage estimation approaches and models.
The results showed that the flood damage can be highly influenced by the quality of the
input data . Using the hazard map developed in this study, that contains more areas affected by
floods, the value of damages is increasing considerably particularly in urban areas. Furthermore,
it was demonstrated that the coarse resolution of the existing land use m aps at large scale (e.g.
Corine land cover) induce large uncertainties in damage calculation, particularly for industrial
areas. It was demonstrated that the Corine land cover present errors regarding the identification of
industrial land use class, undere stimating this areas.
The main limitation of the proposed approach is related to the fact that the flood defense
measures are not considered in the analysis and the simplified procedures may decrease the
accuracy of the results. However, the proposed met hodology, applied and tested for the entire
territory of Romania, can be applied over large areas with limited availability of data and where
hydraulic analysis cannot be conducted, offering high -resolution results. Furthermore, the
homogeneity of data off er comparable and consistent results.

In this study, the methodology was applied in order to estimate the potential damage for a
return period of 100 years. However, the methodology can be used for flood risk assessment by
applying it for different return periods.

5. General c onclusions
Floods are phenomena that occur annually worldwide, being the main natural hazard with
a devastating impact on societies (I. Escuder -Bueno et al., 2012; Schober et al., 2013). In recent
years the studies (te Linde et al., 2010; BUBECK and Krei bich, 2011; BUBECK P. et al., 2011;
Escuder -Bueno et al., 2012, etc.) have shown that the frequency but also the economic damage
caused by floods is increasing in many regions (Albano et al., 2017). The main causes are
associated with climate and socio -economic change. The first one refers to changes in
environmental conditions like rainfall patterns and intensities while the second one refers to the
growth of population, assets and economic activity in flood prone areas and land use change .
In this contex t, the flood risk management has faced an increasing development in order to
deal with the devastating impacts of floods. The focus on more base -risk approaches and the need
of comprehensive flood risk assessment methodologies was also supported by the imp lementation
of the Flood Directive. The current flood risk methodologies are integrating in the analysis both
hazard and consequence information for the development of hazard and risk maps. In this way, a
better understanding of what can be expected is ach ieved and therefore preparing and protection
measures in order to reduce the risk can be applied.
This thesis focused at improving the understanding and the knowledge of flood risk
assessment process by provideng and applying a quantitative framework me thodology for damage
estimation and analysing the related uncertainties. The scope of the thesis was stated as follows:
to quantitatively analyze the flood damage and its related uncertainties in data -scarce
environments, using innovative tools and methods that can offer cost -effective (instead of
expensive and time -consuming approach) and repeatable (transparent) solution for the flood area
delineation and damage calculation .
The thesis achieved this scope through a series of objectives:
 by presentin g a theoretical background regarding the flood risk and damage assessment,
the current tools and methods that are used in the literature;
 by proposing free and open source GIS tools for hazard, exposure and damage estimation;
 by applying the selected tools for the quantitative estimation of flood damages in data –
scarce environments;
 by using free available and high -resolution data;

 by presenting an uncertainty and sensitivity analysis regarding the flood damage
assessment.
In order to provide a thorough und erstanding of flood risk assessment process, the main
concepts related to floods, flood risk and flood damage were presented and synthesized. The
terminology regarding flood hazard and risk is described, as well as the methodology of flood
damage assessmen t. The different types of damages and the factors that are influencing them are
presented. There are a variety of tools and models that are used for hazard, exposure and damage
analysis, therefore a review was done with the aim of underlining the advantage s an limitations of
different tools.
In the case study Flood damage assessment and uncertainties analysis. The case study of
2006 flood in Ilișua basin in Romania , a quantitative flood damage assessment was presented,
combining hazard and exposure informat ion and using depth -damage functions. In order to
perform the damage simulations a GIS tool, called FloodRisk was applied. An uncertainty and
sensitivity analysis was further performed in order to assess the impact of damage functions on the
results. It is acknowledge that the transferability of damage functions in different regions can
induce great uncertainties in the analysis. Therefore the estimations of this kind of uncertainties
and their communication to the stakeholders is an important part of flood risk management, this
information being useful in the decision -making process.
In the second study, GIS tools for large -scale analysis of economic flood damage in data –
scarce environments. The case study of Romania , a national quantitative flood damage as sessment
was conducted (for a return period of 100 years). The main outcomes of this study are represented
by:
– A national hazard map , that contains the flood area and water depth for all the streams in
Romania, including the secondary ones that usually a re not considered in large -scale analysis.
– A national land use map containing seven land use classes that were extracted from
Landsat 8 multispectral satellite images
– Damage results at national level that were obtained using the above mentioned maps;
furthermore, the damages were also calculated using standard data (Corine Land Cover and the
pan-European JRC hazard map), and a comparison analysis was conducted.
Therefore, in this study a preliminary methodology for the estimation of flood damages in
data-scarce environments was applied. In order to obtain the hazard map, a GIS tool (GFA tool)

that is using the geomorphology of the basin was used. Using the DEM of the study areas, the
geomorphological features of this area can be extracted and used for the flood delineation and
water depth calculation. This method is easy to apply and can provide extended flood maps that
includes all the streams from the basins that are analysed, offering in this way a complete analysis
over large -areas. Furthermore for the analysis it was used a high -resolution DEM (30 m), the
current studies in the field using data with a resolution ranging from 100 m to 1 km. Therefore,
more accurate and reliable results are obtained.
A land use map for the flood affected areas was de veloped in this study. For this purpose
the Landsat 8 multi -spectral satellite images with a resolution of 30 m were used along with
machine -learning classification algorithms in order to detect the land use classes for the study area.
For the training of the algorithm, the Urban Atlas database was used. This method can be used for
the identification of land use classes over large areas with limited availability of data.
The damages were quantitatively estimated using the pan -European JRC depth -damage
curves along with the FloodRisk tool. Four scenarios were considered regarding the input data for
the damage estimation: JRC water depth and CLC ; GFI water depth and Landsat 8 land use; JRC
water depth and Landsat 8 land use and GFI water depth and CLC . It was demonstrated that when
the GFI map is used, the damages increase, particularly in urban and industrial areas. This is
explained by the fact that larger areas are affected by floods. Furthermore the CLC database induce
great uncertainties in the damage res ults, particularly the industrial areas, this areas being
underestimated by the Corine database.
The proposed methodology can be used for cost -benefit analysis, offering quantitative
information regarding the flood damages at national level. It can be use d in the decision -making
process, identifying the high risk areas and therefore prioritizing the risk reduction measures and
strategies. Furthermore the use of free publicly available tools and data allow the further scientific
development of this approach , improving the knowledge in the field.

6. Potential applications for stakeholders and future recommendation

The devastating impact of floods in the past years triggered the awareness and interest
regarding the need of effective flood risk reduction measures and strategies. In this context, the
stakeholders are facing a difficult task needing up to data knowledge, methods and techniques in
order to be able to respond to the growing requirements for an improved and unitary flood risk
management. Moreover, the lack of data in many areas represent a real issue, the traditional method
to obtain this data being expensive and time -consuming.
The flood risk assessment at meso -scale is used for regional hazard and risk mapping,
helping the autho rities to identify high -risk areas, to prioritize investments and to implement
effective flood risk reduction measures. It also play an important role in the decision -making
process and it can be used for planning strategies. ROrisk connection? to be fores een
Moreover, stakeholders such as the governments and the insurance industry need
comparable and consisted data over large areas for national and global policies and for the
prioritization of investments at national level . The governments is using the haz ard information in
order to implement flood damage reduction measures, for emergency operations and for land use
policies. For this purpose, efficient methods and tools are needed for hazard and risk mapping. On
the other hand, this information are useful for insurance companies in order to establish the price
for flood risk insurance. The frequency and the damages produced by flood are expected to
increase as well as the number of flood exposed assets; therefore, the number of insurances will
increase, the need of accurate an undated data regarding the flood risk being of upmost importance
for insurance companies.
However, even though the research in the field of flood risk management is constantly
improving, there is a gap between the scientific knowledge and the implementation of this
knowledge for common practices. Therefore, the research findings should be delivered and
materialized in methods and tools that can be applied in a useful way be the stakeholders. On the
other hand, the research goals should be relevant to the stakeholders’ needs. These facts can be
accomplished through a collaborative approach that promote the common involvement of scientist
and stakeholders in the flood risk management process.
In this study, the proposed methodology was u sed for the estimation of flood damages for
a return period of 100 years. As a future research aim, this methodology can be applied for different

return periods in order to assess the flood risk at national scale. This could offer a better
understanding of the high -risk area distribution on the territory of Romania.
Another aim for further researches is the improvement of the data regarding the asset value,
this data representing an important source of uncertainties in flood risk assessment process. For
large scale, generalised data are used, however they may not be representative for the entire
analysed territory. Therefore, there is a need for a detailed database containing this information for
different categories and type of assets (e.g. land use classe s) as well as for different locations. The
damage functions represent another component of the analysis that must be improved, this
approach being still new in the research field. Few areas have developed specific damage functions
and their transferability in space can induce high uncertainties, therefore a thorough validation
must be done before using them in other areas.
The flood damage assessment should focus more on the indirect damage estimation, since
they represent an important percentage of the to tal damages. Since now the estimation of this
damages has not been approached in many studies, however it would offer a complete assessment
useful in the decision -making process.
One of the limitation of the proposed methodology is the fact that the protec tion measures
are not considered in the analysis. This is a general situation of the current studies in the field, few
research approaches analysing in effects of this measures. Future research should focus on the
integration of structural and non -structur al mitigation measures in the flood risk assessment
process in order to reduce the uncertainties.
Another field of research that can be analysed more closely is the one referring to Natech
events (natural disasters which trigger technological disasters). Romania is a country with an
extended area exposed to floods, containing as well a large number of technological sites (which
contain hazardous substances) (Ozunu et al., 2011; Ozunu et al., 2017). As the industry will
increase and the climate will change the occurrence probability of Natech events caused by floods
will increase as well. The Natech disasters are complex, being difficult to analyses due to the
limited information that is available regarding this type of events. Moreover, the intervention is
more difficult since there are two disaster types taking place in the same time and the resources
(that may be limited) must be used for the management of both situations; the flood event would
make harder the intervention in case of a technological disast er; dangerous substance could by
spilled in water, and therefore the flood will have a greater impact on the assets. All this factors

increase the consequences of the Natech events, comparing with the occurrence of a single event
(natural or technological) . Therefore, there is a need for a better analysis and management of this
type of disasters.

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