Development and Application of Urban Landslide Vulnerability Assessment Methodology PDF
Preview Development and Application of Urban Landslide Vulnerability Assessment Methodology
CClleevveellaanndd SSttaattee UUnniivveerrssiittyy EEnnggaaggeeddSScchhoollaarrsshhiipp@@CCSSUU Civil and Environmental Engineering Faculty Civil and Environmental Engineering Publications 2016 DDeevveellooppmmeenntt aanndd AApppplliiccaattiioonn ooff UUrrbbaann LLaannddsslliiddee VVuullnneerraabbiilliittyy AAsssseessssmmeenntt MMeetthhooddoollooggyy RReeflfleeccttiinngg SSoocciiaall aanndd EEccoonnoommiicc VVaarriiaabblleess Yoonkyung Park Pukyong National University Ananta Man Singh Pradhan Pukyong National University Ungtae Kim Cleveland State University, [email protected] Yun-Tae Kim Pukyong National University Sangdan Kim Pukyong National University Follow this and additional works at: https://engagedscholarship.csuohio.edu/encee_facpub Part of the Civil and Environmental Engineering Commons HHooww ddooeess aacccceessss ttoo tthhiiss wwoorrkk bbeenneefifitt yyoouu?? LLeett uuss kknnooww!! RReeccoommmmeennddeedd CCiittaattiioonn Park, Yoonkyung; Pradhan, Ananta Man Singh; Kim, Ungtae; Kim, Yun-Tae; and Kim, Sangdan, "Development and Application of Urban Landslide Vulnerability Assessment Methodology Reflecting Social and Economic Variables" (2016). Civil and Environmental Engineering Faculty Publications. 77. https://engagedscholarship.csuohio.edu/encee_facpub/77 This Article is brought to you for free and open access by the Civil and Environmental Engineering at EngagedScholarship@CSU. It has been accepted for inclusion in Civil and Environmental Engineering Faculty Publications by an authorized administrator of EngagedScholarship@CSU. For more information, please contact [email protected]. Hindawi Publishing Corporation Advances in Meteorology Volume 2016, Article ID 4572498, 13 pages http://dx.doi.org/10.1155/2016/4572498 Research Article Development and Application of Urban Landslide Vulnerability Assessment Methodology Reflecting Social and Economic Variables YoonkyungPark,1AnantaManSinghPradhan,2 UngtaeKim,3Yun-TaeKim,2andSangdanKim4 1DivisionofEarthEnvironmentalSystemScience,PukyongNationalUniversity,45Yongso-ro,Nam-gu, Busan48513,RepublicofKorea 2DepartmentofOceanEngineering,PukyongNationalUniversity,45Yongso-ro,Nam-gu,Busan48513,RepublicofKorea 3CivilandEnvironmentalEngineering,ClevelandStateUniversity,Cleveland,OH44115,USA 4DepartmentofEnvironmentalEngineering,PukyongNationalUniversity,45Yongso-ro,Nam-gu,Busan48513,RepublicofKorea CorrespondenceshouldbeaddressedtoSangdanKim;[email protected] Received23December2015;Revised29March2016;Accepted7June2016 AcademicEditor:PhilipWard Copyright©2016YoonkyungParketal. This is an open access article distributed under the Creative Commons Attribution License,whichpermitsunrestricteduse,distribution,andreproductioninanymedium,providedtheoriginalworkisproperly cited. Anurbanlandslidevulnerabilityassessmentmethodologyisproposedwithmajorfocusonconsideringurbansocialandeconomic aspects.TheproposedmethodologywasdevelopedbasedonthelandslidesusceptibilitymapsthatKoreanForestServiceutilizes toidentifylandslidesourceareas.Frist,debrisflowsarepropagatedtourbanareasfromsuchsourceareasbyFlow-R(flowpath assessmentofgravitationalhazardsataregionalscale),andthenurbanvulnerabilityisassessedbytwocategories:physicaland socioeconomic aspect. The physical vulnerability is related to buildings that can be impacted by a landslide event. This study considered two popular building structure types, reinforced-concrete frame and nonreinforced-concrete frame, to assess the physicalvulnerability.Thesocioeconomicvulnerabilityisconsideredafunctionoftheresistantlevelsofthevulnerablepeople, triggerfactorofsecondarydamage,andpreparednesslevelofthelocalgovernment.Anindex-basedmodelisdevelopedtoevaluate thelifeandindirectdamageunderlandslideaswellastheresilienceabilityagainstdisasters.Toillustratethevalidityoftheproposed methodology,physicalandsocioeconomicvulnerabilitylevelsareanalyzedforSeoul,Korea,usingthesuggestedapproach.The generaltrendfoundinthisstudyindicatesthatthehigherpopulationdensityareasunderaweakerfiscalconditionthatarelocated atthedownstreamofmountainousareasaremorevulnerablethantheareasinoppositeconditions. 1.Introduction 10m,andthemapsareclassifiedinto5gradesaccordingto theprobabilityoflandslides.Sincethemapsweredeveloped In South Korea, there has been continuous interest in to limit the mountain areas, the impacts of landslides on reducing landslide or debris flow damage because about thedownstreamsidearenotreflectedorevaluated.TheMt. 70% of the Korean territory is covered with mountainous Umyeon landslide that occurred in Seoul, South Korea, in areas. Many researchers have analyzed the factors causing July 2011 suggested that the impact of mountain landslides landslides [1, 2]. Landslide susceptibility maps have been onthedownstreamcityshouldbeconsideredinthelandslide built on the mainland across South Korea by Korea Forest anddebrisflowinformationsystem.FromthelessonsofMt. Service (KFS) and have been used as the basis for the Umyeon landslide, this study was planned to evaluate the studies to predict landslides or reduce the damage. The landslidevulnerabilitytoreflecttheimpactontheurbanareas resolution of KFS landslide susceptibility maps is 10m by underthemountains. 2 AdvancesinMeteorology Seoul China Korea Dobong Japan Gangbuk Nowon Eunpyeong Seongbuk Jungnang Jongno Dongdaemun Seodaemun Gangseo Jung Mapo Seongdong Gwangjin Gangdong Yongsan Yangcheon Yeongdeungpo Dongjak Songpa Guro Gangnam Seocho Geumcheon Gwanak (Kilometers) 00.40.8 1.6 2.4 3.2 (Kilometers) 01.53 6 9 1122 (Kilometers) 01.53 6 9 12 (a) BoroughsinSeoul (b) CensusoutputareaofGangnaminSeoul Figure1:Studyarea,Seoul,SouthKorea,withCensusOutputArea. In view of this, the damage type classification proposed 2.StudyAreaandScale by Smith and Ward [3] provides important insights in the vulnerabilityassessmentframeworkconfiguration.Theyclas- The study area is Seoul, the capital of South Korea, which sifiedthetypesofdamagefromnaturaldisastersintodirect has a complicated socioeconomic infrastructure and a high and indirect damage. Representative examples of the direct populationdensity.Incountriesmostofthecriticalnational infrastructureishighlyconcentratedinonesinglecity(such damageisthedamageoflifeandproperty.Ontheotherhand, as South Korea), and the vulnerability evaluation methods examplesoftheindirectdamagecanbetheintangibledamage applied to many cities by previous studies are not suitable caused by traffic disruptions. In order to evaluate properly to the current study area. In order to properly evaluate theurbanvulnerabilitytonaturaldisasters,varioustypesof the vulnerability of a large metropolitan area, it should be damage should be reflected in the vulnerability assessment evaluatedinmuchhigherspatialresolution. framework[4]. Korea National Statistical Office (KNSO) has set up Although many studies have evaluated the damage of “Census Output Area (COA)” as a minimal spatial scale buildings due to a landslide [5–7], studies evaluating the to publish national standard statistical data in South Korea damage of life or secondary damage are not sufficient to [12]. The size is determined by the amount of people and provide a guideline. In recent years, however, some studies the social homogeneity resulting in an average population on the socioeconomic vulnerability assessment considering sizeofabout500peopleperCOA.ThereforetheCOAlevel thedamageoflifeorsecondarydamagehavebeenattempted wasconsideredthebasicmappingunitforthevulnerability [8–11]. Those studies mainly focused on evaluating the assessmentinthisstudy.Seouliscomposedof16,230COAs. vulnerability in terms of sensitivity to natural disasters and 2 2 The area of COAs ranges from 0.00012km to 10.10km abilitytorespondtothem. 2 (average 0.037km ). Figure 1 shows the locations of Seoul Inthisstudy,acombinedmethodologyforevaluatingthe with 25 boroughs and COAs constituting the Gangnam urbanphysicalandsocioeconomicvulnerabilitytolandslides region in the boroughs. Other boroughs also show similar isproposed.Urbanlandslidevulnerabilityisassessedbytwo COAdistributiontoGangnam. categories; physical and socioeconomic aspect. The damage ofbuildingsbylandslidesisconsideredtobeaproxyvariable 3.UrbanLandslideVulnerabilityAssessment representing the physical vulnerability. On the other hand, thesocioeconomicvulnerabilityisevaluatedusinganumber Theproposedurbanlandslidevulnerabilityassessmentpro- of proxy variables that can explain the exposure degree of cess consists of three steps. The first step is to identify vulnerable people in landslide disasters, factors causing a potentiallandslidehazardareas,whichcanbecarriedoutby variety of secondary damage, and disaster preparedness of combining landslide susceptibility maps and Flow-R model localgovernments. [13]. The second step is to assess separately physical and AdvancesinMeteorology 3 Landslide susceptibility map (Kilometers) 01.53 6 9 12 Landslide susceptibility map Very high Low High Very low Predefined source using rank 1 Moderate Figure2:Identificationofpredefinedsourceareasusinglandslidesusceptibilitymap. socioeconomic vulnerability based on identified potential Table1:SelectedlandslidedisasteralgorithmsofFlow-R. landslide susceptibility areas. As a final step, the urban Propagationroutine Appliedmethod landslide vulnerability is generated by combining physical andsocioeconomicvulnerability. Spreadingalgorithm Flowdirectionalgorithm Holmgren[14]modified 3.1. Potential Landslide Areas Identification. Vulnerability Persistencefunction Inertialparameter assessmentunderlandslidedisasterrequiresinformationon Frictionlaw Simplifiedfriction-limitedmodel the propagation extent and intensity by debris flows. Such information on debris flow susceptibility mapping can be obtainedbyregionalscalerunoutsimulation.Sincelandslides cases.“Predefinedsource”isdefinedbyrank1areainthemap are caused by various natural factors, landslide analysis in toapplyextremecase(redgridsinFigure2).Theresolution regionalscaleisdifficult.Hortonetal.[13]developedadis- ofDEMandpredefinedsourceareasis10mby10m. tributedempiricalmodel,Flow-R, forflowpathassessment Flow-Rprovidesavarietyofalgorithmstoanalyzedebris ofgravitationalhazardsataregionalscale.Themodelisavail- flow.Table1showsthealgorithmsappliedtoanalyzelandslide ablefreeofchargeathttp://www.flow-r.org/.Oneadvantage disasterinthisstudy.Spreadingalgorithmscontrolthepath of this model is that the amount of data required for the andthespreadingofdebrisflow.Frictionlawdeterminesthe simulation is small. A Digital Elevation Model (DEM) may runout distance. All algorithms in Flow-R are described in besufficienttoidentifypotentiallandslidesourceareasand detail by Horton et al. [13]. The parameters for simulating toprocessthepropagation.Datasuchasslope,curvature,and Flow-Rhavetobecalibratedandverifiedusingactualdata. flowaccumulationarerequiredformoreaccurateanalysis. ButsuchdatasetisnotprovidedinSeoul.Sothisstudyhas This study applies Flow-R to obtain two landslide char- nochoicebuttousethereferencevaluesfromHortonetal. acteristics:spreadingprobabilityandimpactpressure.DEM [13]. and predefined sources are used for Flow-R simulation. TheresultofFlow-Rsimulationisthetotalareathatcan Predefined source is potential grids where landslide may bepotentiallypropagatedbydebrisflowswithanassociated occur. The predefined source used in this study is obtained susceptibilityvalueandkineticenergy.Theresolutionofthe byusinglandslidesusceptibilitymaps.Themapsdeveloped outputisthesameasthatoftheinputdata.Thesusceptibility byKFSaredisplayedbyclassifyingthelandslideprobability value is used to extract COAs which are influenced by to five ranks. Rank 1 areas in the maps have the highest landsliderunout.Equation(1)isusedtoestimatetheimpact probabilityoflandslideoccurrence. pressure(p,kPa)ineachgrid[15]: Inotherwords,landslideeventisthemostfrequentinthe √2𝐸 𝜌 area.ThisstudyisthefirststepinSouthKoreaabouturban impact pressure (𝑝)= kin 𝑏, (1) landslide vulnerability. It is necessary to study the extreme 1000 4 AdvancesinMeteorology Interpretation of landslide using Flow-R Occurrence Kinetic range energy Extract of COAs which are Classification of affected COAs affected landslide depending on structure type Reinforced- Nonconcrete COA which is affected COA concrete frame frame landslide Building Application of vulnerability function Landslide Estimation of vulnerability based on physical characteristics Figure3:Procedureofphysicalvulnerabilityassessment. where𝐸kin isthekineticenergycalculatedbyFlow-Rinter- Table2:Vulnerabilityfunctions[6]. nallyand𝜌𝑏isthebulkdensityofdebrisflow(2,239.17kg/m3 Frametype Vulnerabilityfunction was used in this study). If the impact pressure of landslide exceeds about 34kPa, houses built with bricks or woods Non-RCframe 𝑉=1−𝑒−0.0010𝑝2.227 wouldbedestroyedcompletely[6]. RCframe 𝑉=1−𝑒−0.0005𝑝1.690 Note.𝑉:physicalvulnerability;𝑝:impactpressure(kPa). 3.2. Physical Landslide Vulnerability Assessment. When a landslideoccurs,thephysicalcharacteristicssuchasvelocity, spatiallyaveragedimpactpressureintheidentifiedCOAand depth, and impact pressure are determining the level of vulnerabilitycurves. damage of buildings. Many studies have been conducted to Two kinds of vulnerability curves, which have been find the relationship between physical characteristics and developed by Kang and Kim [6], are applied in accordance the damage of various structures [16–18]. These previous witheachofthetwobuildingstructuretypes(Figure4).The studies mainly focused on creating a vulnerability curve by physical vulnerability therefore ranges from 0 (the lowest correlatingtherelationshipbetweenlandslidecharacteristics vulnerability) to 1 (the highest vulnerability) as shown in and the damage of buildings. Based on those findings, Table 2. The highest vulnerability (value equals 1) means this study developed vulnerability curves for two different buildingsarebrokendowncompletelywhenlandslideevent structure types in the study area, and then the curves were happens. used to assess physical vulnerability. Figure 3 shows the procedureofthephysicalvulnerabilityassessment. The first stage of the physical vulnerability assessment 3.3.SocioeconomicVulnerabilityAssessment. Socioeconomic is to identify COAs affected by landslides using Flow-R vulnerability aims to evaluate the social and economic simulations and to calculate the spatially averaged impact factors that may be affected by landslides. Socioeconomic pressure in identified COAs. The second stage is to classify vulnerability should be evaluated by a variety of factors thebuildingsinanidentifiedCOAintotwocategories:non- with various perspective. This can be carried out using an reinforcedconcrete(non-RC)frameandreinforced-concrete index-basedmodel[8].Inthisstudy,themodifiedversionto (RC) frame. Finally, the physical vulnerability assessment matchtheconditionsinKoreabasedonthesocioeconomic is to calculate the physical vulnerability by linking the vulnerabilityassessmentframeworkoflandslidesdeveloped AdvancesinMeteorology 5 1 Table 3: Proxy variables and their weights in socioeconomic vulnerabilityassessmentmodel. 0.9 0.8 Social-economicvulnerabilityindex Weights 0.7 DSI(demographicandsocialindex):31% Agedistribution 13.8% bility 0.6 Numberofworkerswhomaybeexposedtodisasters 26.4% a 0.5 er Populationdensity 24.4% n Vul 0.4 Foreignerratio 6.8% 0.3 Educationlevel 9.4% Housingtype 19.2% 0.2 STI(secondary-damage-triggeringindex):34% 0.1 Numberofpublicoffices 14.7% 0 Roadarearatio 25.8% 0 50 100 150 200 250 Impact pressure (kPa) Numberofelectronicsupplyfacilities 28.2% Schoolarearatio 11.4% Nonreinforced concrete Commercialandindustrialarearatio 19.9% Reinforced concrete PRI(preparationandresponseindex):35% Figure4:Vulnerabilitycurvesdependingonimpactpressure[6]. Disastersfrequency 12.4% Internetpenetrationrate 8.6% Numberofdisasterpreventionfacilities 25.8% Perceivedsafety 27.8% bySafeland[19]isproposed.Theproposedmodelcaneval- Numberofmedicaldoctors 13.2% uate demographic factors, economic factors, and landslide Financialindependenceoftheborough 12.2% disasterpreparednessandresponsecapabilities.Thepotential proxyvariablesareidentifiedasagedistribution,population density, housing type, personal assets, risk perception, the presence of disaster warning system, and so on. Those variablesaremainlyselectedbyaliteraturereviewandexpert and the spatial resolution of the data is COA. However, interviews. The procedure of socioeconomic vulnerability the resolution of the proxy variables for PRI is a borough assessmentisshowninFigure5. (Table 6). Application of PRI is suitable in borough scale The model is composed of a total of three subindexes: becausedisasterresponsesystemofSouthKoreaisthelocal Demographic and Social Index (DSI), Secondary-Damage- government. TriggeringIndex(STI),andPreparationandResponseIndex Thestructureofthesocioeconomicvulnerabilityismod- (PRI).DSIisevaluatedbysixpopulation-relatedandsocial ified to be suitable in South Korea using the result of them variables that may be affected by natural disasters. For [21]. example,“agedistribution”isclassifiedintovulnerablepeople Tables4,5,and6showthesuggestedsocioeconomicvul- group. The children or elderly people are more vulnerable nerability assessment model with proposed proxy variables thanyoungpeople.Itisthereasontoselect“agedistribution” andcriteriaforrankingofthevariables.Thesevariableshave as proxy variable in DSI (Table 4). “Population density” beenusedinmanypreviousresearches(seecitationsinTables influences vulnerability. If landslide event happens in high 4,5,and6). population density area, it will caused heavy casualties. Finally, the socioeconomic vulnerability index is calcu- Therefore,“populationdensity”canbeusedasproxyvariable latedbyweightedaverageofquantifiedproxyvariablesranks toassesssocioeconomicvulnerability. (using(2)).Theweightsaredeterminedafterextensiveexpert STI is an index to evaluate the indirect damage caused surveybasedontheanalytichierarchyprocessdevelopedby by natural disasters. For instance, when roads are mal- Saaty[22,23].TheweightscanbefoundinTable3.Consider functioning, damage such as traffic jam and destruction of manylifelinesiscaused[20].Publicofficebecomescontrol Total vulnerability score value towerinemergencysituation.Damageinpublicofficecauses ∑Weighted vulnerability score (2) secondary damage due to absence of control systems in = . emergencysituationsuchaslandslideevent.Forthisreason, ∑Weight “thenumberofpublicoffices”isselectedasproxyvariableof STI(Table5). To assess the socioeconomic vulnerability to landslide PRIassessestheabilitytopreventandrespondtonatural disasters,exposureinformationoflandslideshouldbecom- disasters.Allusedproxyvariablesandsubindexesarelisted bined.ByapplyingtheproposedmethodologytotheCOAs inTable3.Statisticaldatathatusedinthisstudyareobtained identifiedfrompreviousFlow-Rsimulation,thevulnerability fromKOSIS(KoreaStatisticalInformationService).Statistics is evaluated for each COA. The vulnerability of the COAs used in this study were compiled in 2010 as a base year, identifiedasnonaffectedareasisassignedtobe0. 6 AdvancesinMeteorology Determination of indicator based model for assessing socioeconomic vulnerability 3 sub indicators 17 proxy variables Collecting data about proxy variables in the model If it is possible, scale is COA Assessment of socioeconomic vulnerability in whole study area Vulnerable Interpretation of landslide using Flow-R information Occurrence range Estimation of socioeconomic vulnerability under landslide Figure5:Procedureofsocioeconomicvulnerabilityassessment. 4.ResultsandDiscussion It can be observed that the COAs located below the mountainous areas with denser residential areas are more 4.1. Physical Vulnerability Assessment. Landslide affected vulnerable to landslide disasters in physical aspect than the COAs were identified by Flow-R simulation, and the cor- nonmountainousorlessdense. responding impact pressure for each identified COA was However, there are clear limitations to the use of the estimated.Figure6showsthespatialdistributionofidentified regionalscaleFlow-Rmodelforobtainingquantitativeoutput landslide affected regions and the corresponding impact thatcanbeusedincombinationwithphysicalvulnerability pressure. The impact pressure was not calculated for the curves. The Flow-R is an empirical model, which does not COAsthatarenotaffectedbylandslidedisasters.Thenumber take into account source volume, entrainment, or rheology. ofidentifiedCOAsis2,249andthemaximumaverageimpact Infurtherresearch,aregionalscalemodelthatcancalculate pressureisestimatedtobe37kPa. thevolumeoflandslideshouldbeapplied[27,28]. The physical vulnerability index is calculated by com- bining the impact pressure presented in Figure 6(b) and vulnerabilityfunctions(Table2)developedbyKangandKim 4.2.SocioeconomicVulnerabilityAssessment. Socioeconomic [6].Inthisstudy,twopatternsofvulnerabilityfunctionswere vulnerability is evaluated by three detailed subindexes appliedtotwobuildingstructuretypes,non-RCandRC.Itis (Table3)andtheresultsofeachvulnerabilityassessmentare notedthattheaveragephysicalvulnerabilityisestimatedto presentedinFigure8. be0.96inthecasethattheimpactpressure37kPaisapplied DSIisavulnerabilityassessmentsubindexrelatedtothe tonon-RC,whileitis0.20fortheRCcase.Figure7showsthe population. In general, it can be seen that COAs where the landslidephysicalvulnerabilityassessmentmapcalculatedby populationdensityishigharemorevulnerable(Figure8(a)). theproposedmethodology. STI is a vulnerability assessment subindex related to the IfphysicalvulnerabilityinacertainCOAisestimatedto impact on other areas of the occurrence of a disaster event be 1, it means that the buildings in the area are completely at a COA. In general, it can be seen that COAs including destroyed when landslide event happens. Therefore, a high major urban areas are more vulnerable (Figure 8(b)). PRI physical vulnerability range (e.g., from 0.841 to 0.960 in isavulnerableassessmentsubindexrelatedtopreparedness Figure8)meansthatthebuildingsintheareaaredamaged andabilitytorespondtodisastersinaborough(Figure8(c)). severelybylandslideevent. Thisvulnerabilityindexisinverselyproportionaltothefiscal AdvancesinMeteorology 7 High: 37 a) P k e ( ur ess pr ct a p m e i g a er v A Low: 0 (Kilometers) (Kilometers) 01.53 6 9 12 01.53 6 9 12 (a) Identifiedregions (b) Impactpressure(kPa) Figure6:ResultsofFlow-Rsimulation. (Kilometers) 01.53 6 9 12 0.000–0.230 0.671–0.840 0.231–0.460 0.841–0.960 0.461–0.670 Figure7:Physicalvulnerabilitytolandslidedisaster. health of local governments. Therefore, the boroughs in a disaster in a socioeconomic perspective. The results shown weakfiscalcondition(northwesternandsoutheasternregions in Figure 8(d) assume that natural disasters have occurred of the study area, Seoul) are classified as a lack of pre- uniformlythroughoutthewholestudyarea,Seoul. parednessandabilitytorespondtodisasters.Socioeconomic To evaluate the vulnerability to landslides specifically vulnerabilityassessmentcalculatedbytheweightedaverage for COAs, Figures 8(d) and 6(a) should be combined. The ofthreedetailedsubindexesisshowninFigure8(d).Overall, combined landslide vulnerability assessment in a socioeco- thesouthernregioninSeoulismorevulnerableagainstthe nomic aspect is shown in Figure 9 as a result. It is suitable 8 AdvancesinMeteorology Table4:CriteriaforrankingofproxyvariablesofDSI. Rankingcriteria Proxyvariable Rank Description 1 Lessthan20%ofpopulationiseitherbetween0and4yearsorover65years 2 20–30%ofpopulationiseitherbetween0and4yearsorover65years Agedistribution[4,11,19,20,24] 3 30–40%ofpopulationiseitherbetween0and4yearsorover65years 4 40–50%ofpopulationiseitherbetween0and4yearsorover65years 5 Over50%ofpopulationiseitherbetween0and4yearsorover65years Lessthan10workersworkineitheragriculture,forestry,mining,transportation,or 1 construction 10–20%ofworkersworkineitheragriculture,forestry,mining,transportation,or 2 construction Numberofworkerswhomaybe 20–30%ofworkersworkineitheragriculture,forestry,mining,transportation,or exposedtodisasters[4,19,20,24] 3 construction 30–40%ofworkersworkineitheragriculture,forestry,mining,transportation,or 4 construction Over40%ofworkersworkineitheragriculture,forestry,mining,transportation,or 5 construction 1 Populationdensityislessthan100people/km2 2 Populationdensityisbetween100and150people/km2 Populationdensity[19,20] 3 Populationdensityisbetween150and300people/km2 4 Populationdensityisbetween300and600people/km2 5 Populationdensityisover600people/km2 1 Foreignerratioislessthan1% 2 Foreignerratioisbetween1and3% Foreignerratio[4,19,20] 3 Foreignerratioisbetween3and4% 4 Foreignerratioisbetween4and5% 5 Foreignerratioisover5% 1 Over30%ofpopulationhaveattendedorareattendingapostsecondaryeducation 2 20–30%ofpopulationhaveattendedorareattendingapostsecondaryeducation Educationlevel[4,19–21] 3 10–20%ofpopulationhaveattendedorareattendingapostsecondaryeducation 4 5–10%ofpopulationhaveattendedorareattendingapostsecondaryeducation 5 Lessthan5%ofpopulationhaveattendedorareattendingapostsecondaryeducation 1 Over45%ofhousingtypeisapartment 2 40–45%ofhousingtypeisapartment Housingtype[19–21] 3 35–40%ofhousingtypeisapartment 4 30–35%ofhousingtypeisapartment 5 Lessthan30%ofhousingtypeisapartment to apply quantitative characteristics of landslide events. But 4.3. Urban Landslide Vulnerability. Urban characteristics thisstudyoverlaidsusceptibilitymapfromFlow-Rduetolack were reflected in vulnerability assessment under landslide ofquantitativedataaboutlandslideevent.Ifthequantitative disaster by combining physical and socioeconomic vul- assessment is able to analyze landslide in regional scales nerabilities. For the combination of the two vulnerability [27, 28], more accurate socioeconomic vulnerability about assessments, the physical vulnerability region (Figure 7) landslidecanbeobtained. was multiplied by the socioeconomic vulnerability region The biggest difference between Figures 8(d) and 9 is (Figure 9) and the region values were normalized between observed in southwestern Seoul. In fact, the southwest area 0 and 1 as one urban vulnerability to landslide. The urban of Seoul is a very high likelihood of a flood disaster area. vulnerability index can be classified into five categories as Therefore, although this region has higher vulnerability to verylow,low,moderate,high,andveryhigh.Theresultofthe common natural disasters, the region may not have high integratedurbanlandslidevulnerabilityassessmentisshown vulnerabilitytolandslide. inFigure10. AdvancesinMeteorology 9 Table5:CriteriaforrankingofproxyvariablesofSTI. Rankingcriteria Proxyvariable Rank Description 1 Therearelessthan5publicoffices 2 Thereare5–10publicoffices Numberofpublicoffices[4,20,25] 3 Thereare10–15publicoffices 4 Thereare15–20publicoffices 5 Thereareover20publicoffices 1 Roadareaislessthan5% 2 Roadareaisbetween5and10% Roadarearatio[20,25] 3 Roadareaisbetween10and15% 4 Roadareaisbetween15and20% 5 Roadareaisover20% 1 Lessthan2electronicsupplyfacilities 2 Thereare2–4electronicsupplyfacilities Numberofelectronicsupplyfacilities[11,20,25] 3 Thereare4–6electronicsupplyfacilities 4 Thereare6–8electronicsupplyfacilities 5 Thereareover8electronicsupplyfacilities 1 Schoolareaislessthan5% 2 Schoolareaisbetween5and10% Schoolarearatio[20] 3 Schoolareaisbetween10and15% 4 Schoolareaisbetween15and20% 5 Schoolareaisover20% 1 Commercialandindustrialareaislessthan0.5% 2 Commercialandindustrialareaisbetween0.5–1% Commercialandindustrialarearatio[4,20] 3 Commercialandindustrialareaisbetween1and2% 4 Commercialandindustrialareaisbetween2and3% 5 Commercialandindustrialareaisover3% Figure 10 indicates that 50% or more of COAs affected damageandindirectdamageshouldbeconsideredinthevul- by landslides (Figure 7) have an urban vulnerability of less nerability assessment processes. In this study, vulnerability than0.3(urbanvulnerabilityisveryloworlow).Thismeans assessmentforlandslidesamongvariousnaturaldisasterswas that these COAs are likely to be in a landslide damage conducted.Inparticular,theproposedmethodologyproperly but relatively less vulnerable. The size of these COAs varies reflectstheurbancharacteristicsintermsofvulnerabilityto 2 2 2 (0.00046km –7.52km , average 0.11km ). On the other natural disasters. Therefore, the results of this study can be hand, the size of very vulnerable COAs where the urban directly utilized for setting the priority of various landslide 2 2 vulnerability is at least 0.6 is small (0.0047km –2.24km , damagereductionprojectsinurbanareas. 2 average0.046km ).ThesizeofaCOAismainlydetermined In the proposed methodology, the possible landslide by the population and their socioeconomic homogeneity. source areas were first identified using existing landslide When the size of a COA is very small, the COA has a very susceptibilitymaps.Flow-Rsimulationprovidedtheextents highpopulationdensityandishighlyurbanized.Iflandslides andimpactpressureofdebrisflowroutedfromtheidentified occurredintheseCOAs,thedamagecausedbythelandslides sourcesareasoflandslide.Theoutcomeswereservedasland- would be amplified. Therefore, these COAs are extremely slide exposure information for physical and socioeconomic vulnerable to the landslide disaster, and disaster mitigation vulnerabilityassessment. measuresmustbetakenfirstinthesepriorityareas. Directdamageduetolandslidedisasterswasconsidered byphysicalvulnerability.Vulnerabilityfunctionsquantifying 5.SummaryandConclusion the degree of damage of buildings were applied to evaluate thephysicalvulnerability.Byapplyingvulnerabilityfunctions For suitable vulnerability assessment of natural disasters tobuildinginformationandimpactpressureofeachaffected including landslides, this study showed that both direct COA,thephysicalvulnerabilityindexwasestimated.
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