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Analysis and quantification of potential consequences in multirisk coastal context at different spatial scales (Normandy, France)

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Abstract

Coastal environment with high interaction between nature and societies is subject to multi-hazard interaction such as landslides, flood or cliff retreat. These territories are characterized by numerous elements at risk located in valley bottoms, front sea or at the outlets of small dry watershed. The aim is to quantify the potential consequences of EaR by integrating multiple hazards exposure at various scale analyses. To quantify the element at risk, three steps have been required. First, an initial rank has been attributed to each class of element at risk at three different scales analysis. Second, the potential consequences are weighted according to environmental dimension. Third, the consequences are combined with a linear combination of criteria in GIS environment. At medium-scale analysis, element at risk highlighted is built-up areas, national road, railway, lifeline and urban centers. At large-scale analysis, consequences concern any kind of house, apartment and complex located on multiple exposure areas. At local scale, consequences concern buildings located on multiple exposure areas with one floor in mixed materials and built after 1980. Thus, this method proposes an approach with multiple scales analysis and by integrating multiple exposure areas to quantify potential consequences. With the environmental dimension in element at risk analysis, it is an intermediate step to traditional risk analysis and, more specifically multirisk analysis without considering in this case the spatial and temporal dimension of hazards.

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References

  • Abbas HB, Routray JK (2013) A semi-quantitative risk assessment model of primary health care service interruption during flood: case study of Aroma locality, Kassala State of Sudan. Int J Disaster Risk Reduct 6:118–128. https://doi.org/10.1016/j.ijdrr.2013.10.002

    Article  Google Scholar 

  • Akbas SO, Blahut J, Sterlacchini S (2009) Critical assessment of existing physical vulnerability estimation approaches for debris flows. Proceedings of landslide processes: from geomorphologic mapping to dynamic modeling, Strasbourg 67

  • Altenbach T (1995) A comparison of risk assessment techniques from qualitative to quantitative. In: ASME pressure and piping conference

  • Birkmann J (2006) Indicators and criteria for measuring vulnerability: theoretical bases and requirements. In: Birkmann J (ed) Measuring vulnerability to natural hazards–towards disaster resilient societies. United Nations University Press, Tokyo, pp 55–77

    Google Scholar 

  • Birkmann J (2007) Risk and vulnerability indicators at different scales: applicability, usefulness and policy implications. Environ Hazards 7:20–31. https://doi.org/10.1016/j.envhaz.2007.04.002

    Article  Google Scholar 

  • Birkmann J, Cardona OD, Carreño ML et al (2013) Framing vulnerability, risk and societal responses: the MOVE framework. Nat Hazards 67:193–211. https://doi.org/10.1007/s11069-013-0558-5

    Article  Google Scholar 

  • Bollin C, Hidajat R (2006) Community-based disaster risk index: pilot implementation in Indonesia. In: Birkmann J (ed) Measuring vulnerability to natural hazards—towards disaster resilient societies. UNU-Press, Tokyo, New York, Paris

    Google Scholar 

  • Cardona OD (2005) Indicators of disaster risk and risk management— main technical report. IDB/IDEA program of indicators for disaster risk management, National University of Colombia, Manizales

  • Carlier B, Puissant A, Dujarric C, Arnaud-Fassetta G (2018) Upgrading of an index-oriented methodology for consequence analysis of natural hazards: application to the Upper Guil catchment (southern French Alps). Nat Hazards Earth Syst Sci 18:2221–2239. https://doi.org/10.5194/nhess-18-2221-2018

    Article  Google Scholar 

  • Carpignano A, Golia E, Di Mauro C et al (2009) A methodological approach for the definition of multi-risk maps at regional level: first application. J Risk Res 12:513–534. https://doi.org/10.1080/13669870903050269

    Article  Google Scholar 

  • Cascini L, Peduto D, Pisciotta G et al (2013) The combination of DInSAR and facility damage data for the updating of slow-moving landslide inventory maps at medium scale. Nat Hazards Earth Syst Sci 13:1527–1549. https://doi.org/10.5194/nhess-13-1527-2013

    Article  Google Scholar 

  • Çetinkaya EK, Alenazi MJF, Peck AM et al (2015) Multilevel resilience analysis of transportation and communication networks. Telecommun Syst 60:515–537. https://doi.org/10.1007/s11235-015-9991-y

    Article  Google Scholar 

  • Chang SE, Yip JZK, van Zijll de Jong SL, Chaster R, Lowcock A (2015) Using vulnerability indicators to develop resilience networks: a similarity approach. Nat Hazards 78:1827–1841. https://doi.org/10.1007/s11069-015-1803-x

    Article  Google Scholar 

  • Chen Y, Dennis SB, Hartnett E et al (2013) FDA-iRISK—a comparative risk assessment system for evaluating and ranking food-hazard pairs: case studies on microbial hazards. J Food Prot 76:376–385. https://doi.org/10.4315/0362-028X.JFP-12-372

    Article  Google Scholar 

  • Chen L, van Westen CJ, Hussin H et al (2016) Integrating expert opinion with modelling for quantitative multi-hazard risk assessment in the Eastern Italian Alps. Geomorphology 273:150–167. https://doi.org/10.1016/j.geomorph.2016.07.041

    Article  Google Scholar 

  • Cooke RM, ElSaadany S, Huang X (2008) On the performance of social network and likelihood-based expert weighting schemes. Reliab Eng Syst Saf 93:745–756. https://doi.org/10.1016/j.ress.2007.03.017

    Article  Google Scholar 

  • Corominas J, van Westen C, Frattini P, Cascini L, Malet J-P, Fotopoulou S, Catani F, Van Den Eeckhaut M, Mavrouli O, Agliardi F, Pitilakis K, Winter MG, Pastor M, Ferlisi S, Tofani V, Hervás J, Smith JT (2013) Recommendations for the quantitative analysis of landslide risk. Bull Eng Geol Environ. https://doi.org/10.1007/s10064-013-0538-8

    Article  Google Scholar 

  • Costa S (1997) Dynamique littorale et risques naturels : L’impact des aménagements, des variations du niveau marin et des modifications climatiques entre la Baie de Seine et la Baie de Somme. Geography, Paris I

  • de Brito MM, Evers M (2016) Multi-criteria decision-making for flood risk management: a survey of the current state of the art. Nat Hazards Earth Syst Sci 16:1019–1033. https://doi.org/10.5194/nhess-16-1019-2016

    Article  Google Scholar 

  • de Tsuzuki MSG, Shimada M (2003) Geometric classification tests using interval arithmetic in b-rep solid modeling. J Braz Soc Mech Sci Eng 25:396–402. https://doi.org/10.1590/S1678-58782003000400012

    Article  Google Scholar 

  • Delahaye D (2003) Du ruissellement érosif à la crue turbide en domaine de grande culture: analyse spatiale d'un phénomène complexe (From concentrated runoff to flash flood in silty loamy plateaux : spatial analysis of a complex phenomena). Bulletin de l'Association de géographes français 80(3):287–301

    Article  Google Scholar 

  • Delahaye D (2008) Modeling the watershed as a complex spatial system: a review. In: Guermond Y (ed) The modeling process in geography. ISTE, London, UK, pp 191–215. https://doi.org/10.1002/9780470611722.ch9

    Chapter  Google Scholar 

  • Dimakos XK, Aas K (2004) Integrated risk modelling. Stat Model 4:265–277

    Article  Google Scholar 

  • Dilley M, Chen RS, Deichmann U, Lerner-Lam AL, Arnold M (2005) Natural disaster hotspots: a global risk analysis. Disaster risk management series, World Bank, Washington, DC

    Book  Google Scholar 

  • Douvinet J (2006) Intérêts et limites des données « CatNat » pour un inventaire des inondations. L’exemple des « crues rapides » liées à de violents orages (Bassin parisien, Nord de la France). Norois 17–30. https://doi.org/10.4000/norois.1733

  • Douvinet J, Planchon O, Cantat O, Delahaye D, Cador J-M (2009) Variabilité spatio-temporelle et dynamique des pluies de forte intensité à l’origine des « Crues Rapides » dans le bassin parisien (France). Climatologie 6

  • Douvinet J, Mallet F, Escudier A et al (2015a) La simulation comme outil d’anticipation des crues rapides dans les petits bassins versants en Seine-Maritime. Revue Internationale de Géomatique 25:99–122. https://doi.org/10.3166/RIG.25.99-122

    Article  Google Scholar 

  • Douvinet J, Van De Wiel MJ, Delahaye D, Cossart E (2015b) A flash flood hazard assessment in dry valleys (northern France) by cellular automata modelling. Nat Hazards 75(3):2905–2929

    Article  Google Scholar 

  • Eidsvig UMK, Kristensen K, Vangelsten BV (2017) Assessing the risk posed by natural hazards to infrastructures. Nat Hazards Earth Syst Sci 17:481–504. https://doi.org/10.5194/nhess-17-481-2017

    Article  Google Scholar 

  • Fisson C, Lemoine JP (2016) les niveaux d’eau en estuaire de seine: risque inondation et changement climatique. GIP Seine-Aval 3:35

    Google Scholar 

  • Fisson C, Lemoine JP, Gandilhon F (2014) Définition de scénarios et modélisation des niveaux d’eau pour la gestion du risque inondation en estuaire de Seine –Synthèse

  • Fonte C, Minghini M, Patriarca J et al (2017) Generating up-to-date and detailed land use and land cover maps using openstreetmap and globeland30. ISPRS Int J Geo-Inf 6:125. https://doi.org/10.3390/ijgi6040125

    Article  Google Scholar 

  • Franci F, Bitelli G, Mandanici E et al (2016) Satellite remote sensing and GIS-based multi-criteria analysis for flood hazard mapping. Nat Hazards 83:31–51. https://doi.org/10.1007/s11069-016-2504-9

    Article  Google Scholar 

  • Fressard M, Thiery Y, Maquaire O (2014) Which data for quantitative landslide susceptibility mapping at operational scale? Case study of the Pays d’Auge plateau hillslopes (Normandy, France). Nat Hazards Earth Syst Sci 14:569–588. https://doi.org/10.5194/nhess-14-569-2014

    Article  Google Scholar 

  • Fressard M, Maquaire O, Thiery Y et al (2016) Multi-method characterisation of an active landslide: case study in the Pays d’Auge plateau (Normandy, France). Geomorphology 270:22–39. https://doi.org/10.1016/j.geomorph.2016.07.001

    Article  Google Scholar 

  • Fuchs S, Birkmann J, Glade T (2012) Vulnerability assessment in natural hazard and risk analysis: current approaches and future challenges. Nat Hazards 64:1969–1975. https://doi.org/10.1007/s11069-012-0352-9

    Article  Google Scholar 

  • Gallina V, Torresan S, Critto A et al (2016) A review of multi-risk methodologies for natural hazards: consequences and challenges for a climate change impact assessment. J Environ Manag 168:123–132. https://doi.org/10.1016/j.jenvman.2015.11.011

    Article  Google Scholar 

  • Gallina V (2015) An advanced methodology for the multi-risk assessment: an application for climate change impacts in the North Adriatic case study (Italy)

  • Garcia-Aristizabal A, Gasparini P, Uhinga G (2015) Multi-risk assessment as a tool for decision-making. In: Pauleit S, Coly A, Fohlmeister S et al (eds) Urban vulnerability and climate change in Africa. Springer, Cham, pp 229–258

    Chapter  Google Scholar 

  • Gaspar-Escribano JM, Iturrioz T (2011) Communicating earthquake risk: mapped parameters and cartographic representation. Nat Hazards Earth Syst Sci 11:359–366. https://doi.org/10.5194/nhess-11-359-2011

    Article  Google Scholar 

  • Glade T, Crozier MJ (2005) Landslide hazard and risk. Wiley, Chichester, West Sussex, England, Hoboken, NJ

    Book  Google Scholar 

  • Godfrey A, Ciurean RL, van Westen CJ, Kingma NC, Glade T (2015) Assessing vulnerability of buildings to hydro-meteorological hazards using an expert based approach—an application in Nehoiu Valley, Romania. Int J Disaster Risk Reduct 13:229–241. https://doi.org/10.1016/j.ijdrr.2015.06.001

    Article  Google Scholar 

  • Graff K, Lissak C, Thiery Y, Maquaire O, Costa S, Medjkane M, Laignel B (2019) Multi-hazards consequences in coastal context at different scale analyses (Normandy, France). Oral communication, Geophysical Research Abstracts, vol 21, EGU2019-2319, Session NH9.11

  • Gumus S, Egilmez G, Kucukvar M, Shin Park Y (2016) Integrating expert weighting and multi-criteria decision making into eco-efficiency analysis: the case of US manufacturing. J Oper Res Soc 67:616–628. https://doi.org/10.1057/jors.2015.88

    Article  Google Scholar 

  • Haklay M (2010) How good is volunteered geographical information? a comparative study of OpenStreetMap and ordnance survey datasets. Environ Plan B: Plann Des 37(4):682–703

    Article  Google Scholar 

  • IPCC (Intergovernmental Panel on Climate Change) (2013) Changements climatiques 2013 (Les éléments scientifiques), Résumé pour les décideurs

  • Jeffers JM (2013) Integrating vulnerability analysis and risk assessment in flood loss mitigation: An evaluation of barriers and challenges based on evidence from Ireland. Appl Geogr 37:44–51

    Article  Google Scholar 

  • Jokar Arsanjani J, Mooney P, Zipf A, Schauss A (2015) Quality assessment of the contributed land use information from openstreetmap versus authoritative datasets. In: Jokar Arsanjani J, Zipf A, Mooney P, Helbich M (eds) Openstreetmap in giscience. Springer, Cham, pp 37–58

    Google Scholar 

  • Kappes MS, Malet J-P, Remaître A et al (2011) Assessment of debris-flow susceptibility at medium-scale in the Barcelonnette Basin, France. Nat Hazards Earth Syst Sci 11:627–641. https://doi.org/10.5194/nhess-11-627-2011

    Article  Google Scholar 

  • Kappes MS, Keiler M, von Elverfeldt K, Glade T (2012a) Challenges of analyzing multi-hazard risk: a review. Nat Hazards 64:1925–1958. https://doi.org/10.1007/s11069-012-0294-2

    Article  Google Scholar 

  • Kappes MS, Papathoma-Köhle M, Keiler M (2012b) Assessing physical vulnerability for multi-hazards using an indicator-based methodology. Appl Geogr 32:577–590. https://doi.org/10.1016/j.apgeog.2011.07.002

    Article  Google Scholar 

  • Khan D, Samadder SR (2015) A simplified multi-criteria evaluation model for landfill site ranking and selection based on ahp and gis. J Environ Eng Landsc Manag 23:267–278. https://doi.org/10.3846/16486897.2015.1056741

    Article  Google Scholar 

  • Kubal C, Haase D, Meyer V, Scheuer S (2009) Integrated urban flood risk assessment—adapting a multicriteria approach to a city. Nat Hazards Earth Syst Sci 9:1881–1895

    Article  Google Scholar 

  • Lai C, Chen X, Chen X et al (2015) A fuzzy comprehensive evaluation model for flood risk based on the combination weight of game theory. Nat Hazards 77:1243–1259. https://doi.org/10.1007/s11069-015-1645-6

    Article  Google Scholar 

  • Laignel B, Costa S, Lequien A et al (2008) Current inputs of continental sediment to the English Channel and its beaches: a case study of the cliffs and littoral rivers of the Western Paris Basin. Zeitschrift für Geomorphologie, Supplementary Issues 52:21–39. https://doi.org/10.1127/0372-8854/2008/0052S3-0021

    Article  Google Scholar 

  • Léone F, Asté J-P, Leroi E (1996) L’évaluation de la vulnérabilité aux mouvements de terrains: pour une meilleure quantification du risque/The evaluation of vulnerability to mass movements: towards a better quantification of landslide risks. Revue de géographie alpine 84:35–46. https://doi.org/10.3406/rga.1996.3846

    Article  Google Scholar 

  • Letortu P, Costa S, Bensaid A et al (2014) Vitesses et modalités de recul des falaises crayeuses de Haute-Normandie (France): méthodologie et variabilité du recul. Géomorphologie: relief, processus, environnement 20:133–144. https://doi.org/10.4000/geomorphologie.10588

    Article  Google Scholar 

  • Li C, Cheng X, Li N, Du X, Yu Q, Kan G (2016) A framework for flood risk analysis and benefit assessment of flood control measures in urban areas. Int J Environ Res Publ Health 13(8):787

    Article  Google Scholar 

  • Lissak C, Maquaire O, Puissant A, Malet J-P (2013) Landslide consequences and post crisis management along the coastal slopes of Normandy, France. In: Margottini C, Canuti P, Sassa K (eds) Landslide science and practice. Springer, Berlin, pp 23–30

    Google Scholar 

  • Lissak C, Maquaire O, Malet J-P et al (2014) Airborne and ground-based data sources for characterizing the morpho-structure of a coastal landslide. Geomorphology 217:140–151. https://doi.org/10.1016/j.geomorph.2014.04.019

    Article  Google Scholar 

  • Liu Z, Nadim F, Garcia-Aristizabal A et al (2015) A three-level framework for multi-risk assessment. Georisk Assess Manag Risk Eng Syst Geohazards 9:59–74. https://doi.org/10.1080/17499518.2015.1041989

    Article  Google Scholar 

  • Malet J-P, Thiery Y, Maquaire O, Puissant A (2006) Analyse spatiale, évaluation et cartographie du risque glissement de terrain. Revue Internationale de Géomatique 16:499–525

    Article  Google Scholar 

  • Maquaire O (1990) Les mouvements de terrain de la côte du calvados recherche et prévention. BRGM, Orléans

    Google Scholar 

  • Maquaire O, Weber C, Thiery Y, et al (2004) Current practices and assessment tools of landslide vulnerability in mountainous basins. Identification of exposed elements with a semi-automatic procedure. In: Proceedings of 9th international symposium on landslides. Balkema, Rio de Janeiro, Brazil, pp 171–176

  • Marzocchi W, Garcia-Aristizabal A, Gasparini P et al (2012) Basic principles of multi-risk assessment: a case study in Italy. Nat Hazards 62:551–573. https://doi.org/10.1007/s11069-012-0092-x

    Article  Google Scholar 

  • MEEM/MLHD (2016) Plans de Prévention des Risques Naturels (PPR), Ministère de l’Environnement, de l’Energie et de la Mer (MEEM), Ministère du Logement et de l’Habitat Durable (MLHD). La Documentation Française

  • Milanesi L, Pilotti M, Belleri A et al (2018) Vulnerability to flash floods: a simplified structural model for masonry buildings. Water Resour Res 54:7177–7197. https://doi.org/10.1029/2018WR022577

    Article  Google Scholar 

  • Mouroux P, Brun BL (2006) Presentation of RISK-UE Project. Bull Earthq Eng 4:323–339. https://doi.org/10.1007/s10518-006-9020-3

    Article  Google Scholar 

  • Muis S, Güneralp B, Jongman B et al (2015) Flood risk and adaptation strategies under climate change and urban expansion: a probabilistic analysis using global data. Sci Total Environ 538:445–457. https://doi.org/10.1016/j.scitotenv.2015.08.068

    Article  Google Scholar 

  • Nezarat H, Sereshki F, Ataei M (2015) Ranking of geological risks in mechanized tunneling by using Fuzzy Analytical Hierarchy Process (FAHP). Tunn Undergr Space Technol 50:358–364. https://doi.org/10.1016/j.tust.2015.07.019

    Article  Google Scholar 

  • Papathoma-Köhle M, Neuhäuser B, Ratzinger K et al (2007) Elements at risk as a framework for assessing the vulnerability of communities to landslides. Nat Hazards Earth Syst Sci 7:765–779

    Article  Google Scholar 

  • Papathoma-Köhle M, Gems B, Sturm M, Fuchs S (2017) Matrices, curves and indicators: a review of approaches to assess physical vulnerability to debris flows. Earth Sci Rev 171:272–288. https://doi.org/10.1016/j.earscirev.2017.06.007

    Article  Google Scholar 

  • Penadés-Plà V, García-Segura T, Martí J, Yepes V (2016) A review of multi-criteria decision-making methods applied to the sustainable bridge design. Sustainability 8:1295. https://doi.org/10.3390/su8121295

    Article  Google Scholar 

  • Petrucci O, Gullà G (2010) A simplified method for assessing landslide damage indices. Nat Hazards 52:539–560. https://doi.org/10.1007/s11069-009-9398-8

    Article  Google Scholar 

  • Planton S, Le Cozannet G, Cazenave A, Costa S, Douez O, Gaufrès P, Hissel F, Idier D, Laborie V, Petit V, Sergent P (2015) Le climat de la France au XXIe siècle - Changement climatique et niveau de la mer : de la planète aux côtes françaises. Vol. 5 (Action 1 du Plan national d’adaptation au changement climatique-MEEM),. Sous la direction de J. Jouzel, CEA - LSCE/IPSL

  • Ponniah P (2001) Data warehousing fundamentals: a comprehensive guide for IT professionals. Wiley, New York

    Book  Google Scholar 

  • Puissant A, Van Den Eeckhaut M, Malet J-P, Maquaire O (2013) Landslide consequence analysis: a region-scale indicator-based methodology. Landslides 11:843–858. https://doi.org/10.1007/s10346-013-0429-x

    Article  Google Scholar 

  • Saaty TL (2006) Rank from comparisons and from ratings in the analytic hierarchy/network processes. Eur J Oper Res 168:557–570. https://doi.org/10.1016/j.ejor.2004.04.032

    Article  Google Scholar 

  • Saha AK, Gupta RP, Sarkar I et al (2005) An approach for GIS-based statistical landslide susceptibility zonation? With a case study in the Himalayas. Landslides 2:61–69. https://doi.org/10.1007/s10346-004-0039-8

    Article  Google Scholar 

  • Sahoo B, Bhaskaran PK (2018) Multi-hazard risk assessment of coastal vulnerability from tropical cyclones—a GIS based approach for the Odisha coast. J Environ Manag 206:1166–1178. https://doi.org/10.1016/j.jenvman.2017.10.075

    Article  Google Scholar 

  • Saint-Martin C, Javelle P, Vinet F (2018) DamaGIS: a multisource geodatabase for collection of flood-related damage data. Earth Syst Sci Data 10:1019–1029. https://doi.org/10.5194/essd-10-1019-2018

    Article  Google Scholar 

  • Scheuer S, Haase D, Meyer V (2011) Exploring multicriteria flood vulnerability by integrating economic, social and ecological dimensions of flood risk and coping capacity: from a starting point view towards an end point view of vulnerability. Nat Hazards 58:731–751. https://doi.org/10.1007/s11069-010-9666-7

    Article  Google Scholar 

  • Tayyebi A, Pijanowski BC, Tayyebi AH (2011) An urban growth boundary model using neural networks, GIS and radial parameterization: an application to Tehran, Iran. Landsc Urban Plan 100:35–44. https://doi.org/10.1016/j.landurbplan.2010.10.007

    Article  Google Scholar 

  • Totschnig R, Sedlacek W, Fuchs S (2011) A quantitative vulnerability function for fluvial sediment transport. Nat Hazards 58(2):681–703

    Article  Google Scholar 

  • Turki I, Laignel B, Chevalier L et al (2015) On the investigation of the sea-level variability in coastal zones using SWOT satellite mission: example of the Eastern English Channel (Western France). IEEE J Sel Top Appl Earth Obs Remote Sens 8:1564–1569. https://doi.org/10.1109/JSTARS.2015.2419693

    Article  Google Scholar 

  • Uzielli M, Catani F, Tofani V, Casagli N (2015) Risk analysis for the Ancona landslide—II: estimation of risk to buildings. Landslides 12(1):83–100

    Article  Google Scholar 

  • Van der Fels-Klerx HJ, Van Asselt ED, Raley M et al (2018) Critical review of methods for risk ranking of food-related hazards, based on risks for human health. Crit Rev Food Sci Nutr 58:178–193. https://doi.org/10.1080/10408398.2016.1141165

    Article  Google Scholar 

  • Van Westen CJ (2000) The modelling of landslide hazards using GIS. Surv Geophys 21:241–255

    Article  Google Scholar 

  • van Westen CJ, Castellanos E, Kuriakose SL (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Eng Geol 102:112–131. https://doi.org/10.1016/j.enggeo.2008.03.010

    Article  Google Scholar 

  • van Westen C, Kappes MS, Luna BQ et al (2014) Medium-scale multi-hazard risk assessment of gravitational processes. In: Van Asch T, Corominas J, Greiving S et al (eds) mountain risks: from prediction to management and governance. Springer, Netherlands, pp 201–231

    Chapter  Google Scholar 

  • Vojinovic Z, Hammond M, Golub D et al (2016) Holistic approach to flood risk assessment in areas with cultural heritage: a practical application in Ayutthaya, Thailand. Nat Hazards 81:589–616. https://doi.org/10.1007/s11069-015-2098-7

    Article  Google Scholar 

  • Wei S-H, Chen S-M (2009) Fuzzy risk analysis based on interval-valued fuzzy numbers. Expert Syst Appl 36:2285–2299. https://doi.org/10.1016/j.eswa.2007.12.037

    Article  Google Scholar 

  • Yin J, Yu D, Yin Z et al (2016) Evaluating the impact and risk of pluvial flash flood on intra-urban road network: a case study in the city center of Shanghai, China. J Hydrol 537:138–145. https://doi.org/10.1016/j.jhydrol.2016.03.037

    Article  Google Scholar 

  • Yoon E, Lee D, Kim H et al (2017) Multi-objective land-use allocation considering landslide risk under climate change: case study in Pyeongchang-gun, Korea. Sustainability 9:2306. https://doi.org/10.3390/su9122306

    Article  Google Scholar 

  • Zahran S, Brody SD, Peacock WG et al (2008) Social vulnerability and the natural and built environment: a model of flood casualties in Texas. Disasters 32:537–560. https://doi.org/10.1111/j.1467-7717.2008.01054.x

    Article  Google Scholar 

  • Zahran E-SMM, Tan SJ, Yap YH et al (2017) A novel approach for identification and ranking of road traffic accident hotspots. MATEC Web Conf 124:04003. https://doi.org/10.1051/matecconf/201712404003

    Article  Google Scholar 

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Acknowledgements

This research was supported by the COMUE Normandy University which has financed the Ph.D. grant of KG and by the ANR scientific project “RICOCHET: multi-RIsk assessment on Coastal territory in a global CHange context” funded by the French Research National Agency (ANR-16-CE03-0008).

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Authors and Affiliations

  1. Normandie Univ, Unicaen, CNRS, LETG-Caen, 14000, Caen, France

    K. Graff, C. Lissak, O. Maquaire & S. Costa

  2. Normandie Univ, Unirouen, CNRS, M2C-Rouen, 76000, Rouen, France

    K. Graff & B. Laignel

  3. BRGM (French Geological Survey) Risk and Prevention Division, 45060, Orléans, France

    Y. Thiery

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  1. K. Graff
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  2. C. Lissak
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  6. B. Laignel
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Graff, K., Lissak, C., Thiery, Y. et al. Analysis and quantification of potential consequences in multirisk coastal context at different spatial scales (Normandy, France). Nat Hazards 99, 637–664 (2019). https://doi.org/10.1007/s11069-019-03763-5

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