Assessment of Landslide Susceptibility by Decision Trees in the Metropolitan Area of Istanbul, Turkey

16 Pages·2014·3.72 MB·English

HindawiPublishingCorporation
MathematicalProblemsinEngineering
Volume2010,ArticleID901095,15pages
doi:10.1155/2010/901095
Research Article
Assessment of Landslide Susceptibility by Decision
Trees in the Metropolitan Area of Istanbul, Turkey
H. A. Nefeslioglu,1 E. Sezer,2 C. Gokceoglu,3
A. S. Bozkir,2 and T. Y. Duman1
1DepartmentofGeologicalResearch,GeneralDirectorateofMineralResearchandExploration,
06520Ankara,Turkey
2DepartmentofComputerEngineering,HacettepeUniversity,Beytepe,06800Ankara,Turkey
3DepartmentofGeologicalEngineering,HacettepeUniversity,Beytepe,06800Ankara,Turkey
CorrespondenceshouldbeaddressedtoC.Gokceoglu,[email protected]
Received29October2009;Accepted29November2009
AcademicEditor:CristianToma
Copyrightq2010H.A.Nefesliogluetal. This is an open access article distributed under the
Creative Commons Attribution License, which permits unrestricted use, distribution, and
reproductioninanymedium,providedtheoriginalworkisproperlycited.
The main purpose of the present study is to investigate the possible application of decision
tree in landslide susceptibility assessment. The study area having a surface area of 174.8km2
locates at the northern coast of the Sea of Marmara and western part of Istanbul metropolitan
area. When applying data mining and extracting decision tree, geological formations, altitude,
slope,plancurvature,profilecurvature,heatloadandstreampowerindexparametersaretaken
into consideration as landslide conditioning factors. Using the predicted values, the landslide
susceptibility map of the study area is produced. The AUC value of the produced landslide
susceptibilitymaphasbeenobtainedas89.6%.AccordingtotheresultsoftheAUCevaluation,
theproducedmaphasexhibitedagoodenoughperformance.
1. Introduction
Thelandslidesusceptibilityandhazardassessmentscanbecarriedouteitherbyusingdirect
mapping techniques or by using indirect mapping techniques. Direct hazard assessment,
in which the degree of hazard is determined by the mapping geomorphologist, based
on his/her experience and knowledge of the terrain conditions (cid:2)39(cid:3). However, indirect
hazard assessment, in which either statistical models or deterministic models are used to
predictlandslideproneareas,basedoninformationobtainedfromtheinterrelationbetween
landscape factors and the landslide distribution (cid:2)39(cid:3). In the recent years, many studies
on the indirect landslide susceptibility assessment have been published depending on the
developments of Geographical Information Systems (cid:4)GISs(cid:5) and digital cartography. It is
2 MathematicalProblemsinEngineering
possible to produce a landslide susceptibility map employing various indirect mapping
techniquessuchascombinationofindexmaps(cid:4)i.e.,(cid:2)1,3,5,26(cid:3)(cid:5),bivariateandmultivariate
statistical analyses (cid:4)i.e., (cid:2)4, 6–15, 17–19, 21, 25, 46(cid:3)(cid:5), neural networks (cid:4)i.e., (cid:2)2, 19, 23,
24, 27, 32, 43(cid:3)(cid:5) and fuzzy approach (cid:4)i.e., (cid:2)28–31, 37(cid:3)(cid:5). It is evident that each landslide
susceptibilityassessmentmethodconsideredbylandslidecommunityhassomeadvantages
anddrawbacks.
According to Miller and Han (cid:2)49(cid:3), similar to many research and application fields,
geographyhasmovedfromadata-poorandcomputation-poortodata-richandcomputation-
rich environment. The scope, coverage, and the volume of digital geographic datasets are
growingrapidly.Moreover,newhighspatialandspectralresolutionremotesensingsystems
and other monitoring devices are gathering vast amounts of georeferenced digital imagery,
video, and sound. Traditional spatial analytical techniques cannot easily discover new and
unexpected patterns, trends, and relationships that can be hidden deep within very large
diverse geographic datasets (cid:2)49(cid:3). At this point, data mining which encompasses variety
of statistical analysis, pattern recognition and machine learning techniques can be used
to overcome the problem when processing very large datasets. When producing landslide
susceptibilitymaps,verylargedatasetsshouldbeprocessed.Asmentionedabove,landslide
susceptibilitymapshavebeenproducedbyusingseveralmethods.Theinvestigationofthe
decisiontreeinlandslidesusceptibilityassessmentconstitutesthemainpurposeofthisstudy.
Forthepurposeofthestudy,decisiontreeisusedtoanalyzethegeographicdatatopredict
landslide susceptible zones on map for the study area, Cekmece area of Istanbul, Turkey
(cid:4)Figure1(cid:5). The study also includes assessment of the landslide conditioning factors, some
theoreticalinformationonthedecisiontreetechnique,andapplicationofthedecisiontrees
tolandslidesusceptibilityassessment.
2. General Characteristics of the Study Area
Thestudyareahavingasurfaceareaof174.8km2 locatesatthenortherncoastoftheSeaof
MarmaraandwesternpartofIstanbulmetropolitanarea(cid:4)seeFigure1(cid:5).TheBuyukCekmece
Lake,andtheKucukCekmeceLakeandDikilitascreekarethewesternandeasternborders
ofthestudyarea,respectively.Theareahasahighseismicity.Inthelastdecade,Turkeyhas
experienced some large earthquakes. More than 300 earthquakes in the region have been
reportedtohaveoccurredbetween2100BCandAD1900(cid:2)55(cid:3).Theactivenorthernbranchof
the North Anatolian Fault Zone (cid:4)NAFZ(cid:5) passes through approximately 9km from south of
theapplicationsite.Inthelast20centuries,betweenIzmitandGulfofSaros,29historically
large(cid:4)between6.3and7.4Ms(cid:5)earthquakesoccurredalongthenorthernbranchoftheNAFZ
(cid:2)35(cid:3).Actually,itmaybeeffortlesslyremarkedthattherearetwomainlandslidetriggers,one
ofwhichisearthquakeandtheotherisheavyrainfall.Ifthecharacteristicsoftheregionare
takenintoconsideration,itispossibletosaythatthelandslidetriggersexistintheapplication
area.
In the study area, summers are hot and slightly rainy while winters are warm
and rainy. The topography of the region and presence of lakes and dams also affect the
weather conditions (cid:4)http://istanbul.meteor.gov.tr/(cid:5). The region receives 85% of the total
annual precipitation in the period of September-May (cid:4)http://istanbul.meteor.gov.tr/(cid:5). In
this study, the data of Florya Meteorology Station, the nearest station to the application
site, was assessed. According to the meteorological data of the period of 1937–1990, the
average monthly rainfall varies between 20.5mm and 102.0mm. The annual precipitation
MathematicalProblemsinEngineering 3
30◦0(cid:3)0”E 35◦0(cid:3)0”E 40◦0(cid:3)0”E 45◦0(cid:3)0”E
Blacksea
Applicationsite N
”
0
(cid:3)0
N ◦0
” 4
0
(cid:3)0
◦0 Ankara
4
N
”
0
N (cid:3)0
◦(cid:3)500” Mediterranean ◦35
3
30◦0(cid:3)0”E 35◦0(cid:3)0”E 40◦0(cid:3)0”E
Figure1:Locationmapofthestudyarea;rectangleinblackpointedoutbythearrowinthefigurecovers
thestudyarea.
varies between 500mm and 1000mm in the region while average annual precipitation of
longperiodoftheFloryaMeteorologyStationis642.4mm.Themaximumdailyprecipitation
wasobservedinIstanbulas88.9kg/m2 (cid:4)http://www.meteor.gov.tr/(cid:5).Aswillbeexplained
in the latter sections, the precipitation is highly effective on the landslide occurrence in the
studyarea.
Various lithological units from Middle-Late Eocene to Quaternary crop out in the
region. The 1/25000 scaled geological map of the study area was prepared by Duman et
al.(cid:2)36(cid:3).ArealdistributionsofthegeologicalformationsofthestudyareaaregiveninTable1
andFigure2.TheKirklarelilimestoneistheoldestrocksofthestudyareafromtheMiddle-
LateEocenewhiletheyoungestunitinthestudyareaisactualalluviums.Inthearea,some
inactive normal faults are typical. The dip values of the beddings of the sedimentary units
in the area are rather low, 5–15 degrees. For this reason, the strikes and the dip directions
exhibit a high variation in short distances. However, there is no considerable folding in the
studyarea(cid:2)17(cid:3).
The altitude values in the study area are between 0 and 200m while the dominant
altitude ranges are 75–100 and 100–125m (cid:4)Table2(cid:5). The study area has a dendritic
drainage pattern, because of presence of soft lithologies and low slope angles. The general
physiographic trend of the area is NW-SE. The range of the slope gradient values is from 0
to57degrees.Finally,theseslopegradientvaluesindicatethatthemajorityoftheregionhas
gentleslopes.
3. Assessment of Landslide Conditioning Factors
In this section, the landslide conditioning factors observed in the study area are explained.
Before the explanations, the data used is given. In the present study, the digital elevation
model(cid:4)DEM(cid:5)producedbyDumanetal.(cid:2)17(cid:3)isused.TheDEMwaspreparedbydigitizing
10maltitudecontoursofthe1/25,000scaletopographicalmap(cid:2)17(cid:3).Mapsofslopegradient,
heatload,altitude,streampowerindex,plancurvature,andprofilecurvatureareproduced
using the DEM in raster format with a pixel size of 25 × 25m2. The lithology map taken
into consideration in this study was prepared by Duman et al. (cid:2)36(cid:3). This vector map
4 MathematicalProblemsinEngineering
640000
Teoi Teoiy
Teoi2
4550000 Tme Tos Yesilbayirfault Teoi 4550000
Tod Tek
Tme
Qa Tos
Toda
Tod Toda
Qa
Tmb
Tmb
0 0
0 0
0 0
0 0
4 4
5 5
4 Toc Qa 4
Tmb
N Tme
W E
S
0 2.5 5 (cid:4)km(cid:5)
640000
Alluvium(cid:4)Qa(cid:5) Kirklarelilimestone(cid:4)Tek(cid:5)
Bakirkoyfm.(cid:4)Tmb(cid:5) Danismentfm.acmalarm.(cid:4)Toda(cid:5)
Ergenefm.(cid:4)Tme(cid:5) Ihsaniyefm.tuffm.(cid:4)Teoi2(cid:5)
Cantakoyfm.(cid:4)Toc(cid:5) Suloglufm.(cid:4)Tos(cid:5)
Danismentfm.(cid:4)Tod(cid:5) Yassiorenlimestone(cid:4)Teoiy(cid:5)
Ihsaniyefm.(cid:4)Teoi(cid:5)
Figure2:Geologicalmapofthestudyarea(cid:2)36(cid:3).
was converted to a raster map with a pixel size of 25 × 25m2 by Duman et al. (cid:2)17(cid:3). A
reliable landslide inventory defining the type and activity of all landslides, as well as their
spatialdistribution,isessentialbeforeanyanalysisoftheoccurrenceoflandslidesandtheir
relationship to environmental conditions undertaken (cid:2)34(cid:3). Therefore, it is possible to say
that a reliable landslide inventory is a crucial part of a landslide susceptibility map among
the parameters employed, because it is the fundamental component of the assessments. In
Turkey,alandslideinventoryprojectatnationalscalehasbeenconductedbytheGeological
ResearchDepartmentoftheGeneralDirectorateofMineralResearchandExploration(cid:4)MTA(cid:5).
When preparing the landslide inventory, to identify the landslides, the vertical black-and-
white aerial photographs of medium scale (cid:4)1:35,000(cid:5), dated 1955–1956, were used (cid:2)17(cid:3).
Whendescribingthetypeandactivityofthelandslidesintheproject(cid:2)38(cid:3),massmovements
MathematicalProblemsinEngineering 5
Table1:Thedistributionofthegeologicalformationswithrespecttolandslideinthestudyarea.
Formation Symbol Gridcellswithlandslides Allgridcells LandslideDensity(cid:4)%(cid:5)
Frequency % Frequency %
Quaternary Qa 1127 2.1 15495 5.54 7.27
Bakirkoyfm. Tmb 4473 8.34 56231 20.1 7.95
Ergenefm. Tme 13617 25.37 71287 25.49 19.1
Cantakoyfm. Toc 5706 10.63 14210 5.08 40.15
Danismentfm. Tod 4728 8.81 20650 7.38 22.9
Ihsaniyefm. Teoi 1810 3.37 43337 15.49 4.18
Kirklarelilimestone Tek 0 0 1613 0.58 0
Danismentfm.
Toda 17151 31.95 34213 12.23 50.13
Acmalarm.
Ihsaniyefm.Tuffm. Teoi2 66 0.12 478 0.17 13.81
Suloglufm. Tos 4996 9.31 21166 7.57 23.6
Yassiorenlimestone Teoiy 0 0 1035 0.37 0
wereclassifiedaccordingtothecinematictypesofclassificationproposedbyVarnes(cid:2)16(cid:3),that
is,flows,falls,andslides.Thelandslidesarealsoclassifiedaccordingtotheirrelativedepths,
asshallow(cid:4)depth<5m(cid:5)anddeepseated(cid:4)depth>5m(cid:5).Landslideactivitieswereclassified
into two groups as active and inactive by Duman et al. (cid:2)38(cid:3). Active landslides are defined
asthosecurrentlymoving;whereasinactiveonesareasrelictaccordingtoWorkingPartyon
WorldLandslideInventoryWP/WLI(cid:2)40(cid:3).Shallowlandslidesareclassifiedasactivebecause
oftheirongoingobservedmovements(cid:2)41(cid:3).
Oneofthemostimportantstagesoflandslidesusceptibilitymappingistodescribethe
factors governing the landslides identified in the area. A landslide susceptibility mapping
procedure for the application site has been performed previously by Duman et al. (cid:2)17(cid:3)
considering the logistic regression technique. Duman et al. (cid:2)17(cid:3) have employed geological
formations,geomorphologicalunits,andrelativepermeabilityofdifferentlithologicalunits,
slope gradient, slope aspect, altitude, plan and profile curvatures, and stream power index
parameters as the landslide conditioning factors. However, in the present study, some
parameters such as geomorphological units and relative permeability of the lithological
unitshavebeeneliminated.Furthermore,severaltopographicparametersusedinthestudy
reflect geomorphological characteristics of the study area. To abstain a redundancy, the
geomorphologicunitsareeliminatedfromthemodel.Theothereliminatedparameterused
by Duman et al. (cid:2)17(cid:3) is the relative permeability of the lithological units. Determination of
thisparameteristoodifficult,sometimesimpossible.Asaresult,therelativepermeabilityis
alsoexcludedfromthemodel.OneofthetopographicparametersusedbyDumanetal.(cid:2)17(cid:3)
isthe slope aspect. Aspect of aslope isthe direction orazimuth of aslope faces. Itstrongly
influencespotentialdirectincidentradiationandtemperature.Untransformedslopeaspectis
apoorvariableforquantitativeanalysis,since1degreeisadjacentto360degrees;thenumeric
valuesareverydifferenteventhoughtheslopeaspectisaboutthesame.Hence,slopeaspect
valuesneedtobetransformedinoneofseveralways,dependingontheprecisionwithwhich
itwasmeasuredandtheenvironmentalfactor(cid:4)s(cid:5)theanalystwouldlikeittorepresent(cid:2)42(cid:3).
In this study, the heat load index (cid:2)42(cid:3) has been used instead of the slope aspect. According
tothedescriptivestatisticsoftheparameter,themeanheatloadindexvalueonthegridcells
with landslides is observed as 0.529 (cid:4)±0.338(cid:5) (cid:4)Table2(cid:5). The other conditioning parameters
6 MathematicalProblemsinEngineering
Table2:Generaldescriptivestatisticsoftopographicalvariableswithrespecttolandslides.
Std.
Data Variable Min. Max. Mean
deviation
Altitude(cid:4)m(cid:5) 0.000 194.680 85.009 45.165
Slopegradient
0.000 57.950 7.966 5.307
(cid:4)◦(cid:5)
Plancurvature –3.130 2.870 −0.013 0.238
Gridcellswithlandslides(cid:4)N(cid:8)53,674(cid:5) Profile
–2.930 3.080 0.023 0.303
curvature
Heatload 0.000 1.000 0.529 0.338
Streampower
0.000 8.330 0.724 0.934
index(SPI)
Altitude(cid:4)m(cid:5) 0.000 200.000 98.229 52.370
Slopegradient
0.000 55.230 4.642 3.924
(cid:4)◦(cid:5)
Plancurvature –4.500 2.690 0.004 0.137
Gridcellswithoutlandslides(cid:4)N (cid:8)226,041(cid:5) Profile –2.920 3.430 −0.004 0.188
curvature
Heatload 0.000 1.000 0.524 0.342
Streampower
0.000 8.670 0.453 0.775
index(SPI)
used in the study are slope gradient, stream power index, plan and profile curvatures, and
altitude.Theseparameterscanalsobeobtainedeasilyfromthedigitalelevationmodelofthe
studyarea.
Thecharactersoflandslidesidentifiedintheregionaremainlydeepseatedandactive.
They are generally located in the lithologies including the permeable sandstone layers and
impermeablelayerssuchasclaystone,siltstone,andmudstonelayers.Thisistypicalforthe
landslides identified in the study area. When considering this finding, it may be said that
one of the main conditioning factors of the landslides in the study area is lithology (cid:2)17(cid:3).
This can be seen clearly in Table1. As can be seen in Table1, the majority of the landslides
(cid:4)approximately 60%(cid:5) occurred in two formations, namely, Danisment formation-Acmalar
member(cid:4)Toda(cid:5)andErgene(cid:4)Tme(cid:5)formation.AccordingtoDumanetal.(cid:2)17(cid:3),anotherfactor
governingthelandslidesinthestudyareaisthesandstonebeddingplanes.Iftheorientation
ofslopeandbeddingplaneisroughlysimilar,somelargelandslidescanoccur.Intheseareas,
initiationofthelandslidesiscontrolledbythebeddingplanesasplanarfailure,andthenin
thedisplacedandaccumulatedmaterial,somerotationallandslidesareobserved(cid:2)17(cid:3).Rarely,
in this material, some earth flows may occur depending on the heavy precipitation (cid:2)17(cid:3).
Besides, permeability differences between sandstones and claystones are highly important
on landslide occurrence in the study area. The sandstones at upper levels are saturated by
thesurfacewatersbecausethesurfacewatersdonotinfiltratetoclaystonesatlowerlevels.
One of the most important topographical factors conditioning landslides is the slope
gradient. In the regional landslide susceptibility or hazard assessments, several researchers
(cid:4)i.e.,(cid:2)7,8,33,44,45(cid:3)(cid:5)tookintoconsiderationstatisticaltechniquesfortheassessmentofslope
gradientintermsoflandslideactivity.Inthepresentstudy,theslopegradientisconsidered
asaconditioningfactorduringtheanalyses.Descriptivestatisticsrevealedthatthelandslides
in the application site typically occur on the gentle slopes (cid:4)Table2(cid:5). Some authors (cid:4)i.e., (cid:2)26,
MathematicalProblemsinEngineering 7
30(cid:3)(cid:5)pointedoutthatthealtitudeisagoodindicatorforthelandslidesusceptibility.Forthis
reason, the altitude has been accepted as a conditioning factor, as well. The mean altitude
valueonthegridcellswithlandslidesisobservedas85.1m(cid:4)±45.2m(cid:5)(cid:4)Table2(cid:5).
The term curvature is generally defined as the curvature of a line formed by
intersectionofarandomplanewiththeterrainsurface(cid:2)40(cid:3).Theinfluenceofplancurvature
onthelanddegradationprocessesistheconvergenceordivergenceofwaterduringdownhill
flow.Inaddition,thisparameterconstitutesoneofthemainfactorscontrollingthegeometry
of the terrain surface where landslides occur (cid:2)47(cid:3). In this study, profile and plan curvature
values have been calculated using a script, namely, Digital Elevation Model Analysis Tool
(cid:4)DEMAT(cid:5)compiledintheAvenuesoftcomputinglanguageofArcViewGISbyBehrens(cid:2)48(cid:3)
and these parameters have been considered as the conditioning factors. While the positive
valuesofslopecurvaturedefinetheconvexity,thenegativeonespresenttheconcavityofthe
terrain surface. The minimum and the maximum profile curvature values were calculated
as −2.930 and 3.080 on the grid cells with landslides, and −2.920 and 3.430 on the grid cells
withoutlandslides(cid:4)Table2(cid:5).Theserangeshavebeenobtainedfortheplancurvaturevalues
onthegridcellswithlandslidesandwithoutlandslidesas−3.130to2.870and−4.500to2.690,
respectively(cid:4)Table2(cid:5).
Thelastparameterconsideredinthepresentstudyisstreampowerindex(cid:4)SPI(cid:5).Itisa
measureoferosivepowerofwaterflowbasedontheassumptionthatdischarge(cid:4)q(cid:5)ispropor-
tionaltospecificcatchmentarea(cid:4)A (cid:5)(cid:2)20(cid:3)(cid:4)3.1(cid:5).Although,theminimumandthemaximum
s
rangesoftheSPIvaluesonthegridcellswithlandslidesandwithoutlandslidesareobserved
asclose(cid:4)Table2(cid:5),themeanSPIvalueforthegridcellswithlandslideswascalculatedmore
than that for the grid cells without landslides (cid:4)Table2(cid:5). Theoretically, the maximum SPI
values are calculated in drainage channels. Therefore, the values of SPI on the grids cells
with landslides should not be expected as high. This hypothetical expectation is supported
bytheobservationofthemaximumSPIvalueonthegridcellswithoutlandslides(cid:4)Table2(cid:5)
SPI(cid:8) A tanβ, (cid:4)3.1(cid:5)
s
whereA isthespecificcatchmentarea(cid:4)m2m−1(cid:5)whileβistheslopegradientindegree.
s
4. Modeling Approach
Data mining involves various techniques such as statistics, neural networks, decision tree,
genetic algorithm, and visualization techniques that have been developed over the years.
Dataminingproblemsaregenerallycategorizedasassociation,clustering,classification,and
prediction (cid:2)50(cid:3). Classification refers to finding rules to assign data items into pre-existing
classes(cid:2)49(cid:3).Associationanalysisisusedtodiscoverpatternsthatdescribestronglyassociated
featuresinthedata.Ontheotherhand,aimofclusteringistofindgroupsofcloselyrelated
observations so that observations that belong to the same cluster are more similar to each
otherthanobservationsthatbelongtootherclusters(cid:2)51(cid:3).
Inpractice,thereareseveraldataminingtoolssuchasOracleDM,SQLServerAnalysis
Services, SPSS Clementine, and SAS Enterprise Miner for commercial use. In the present
study, the decision tree technique is used to predict the landslide susceptibility classes by
employingMicrosoftServer2008AnalysisServices.Decisiontreeisadataminingapproach
that is often used for classification and prediction. Although other methodologies such as
neural network can also be used for classification, decision tree has the advantages of easy
8 MathematicalProblemsinEngineering
Trainingset Learning
algorithm
Tid Attrib1 Attrib2 Class
1 Large 125K Yes Induction
2 Medium 100K No
3 Small 70K No Learnmodel
4 Large 120K Yes
5 Medium 95K No
Testset
Model
Tid Attrib1 Attrib2 Class
11 Small 55K ?
12 Medium 80K ? Deduction
13 Large 100K ? Applymodel
14 Small 95K ?
Figure 3: Schematic illustration of the construction of decision tree by using training data set and an
exampleviewofapredictionontestdata(cid:2)52(cid:3).
interpretation and understanding for the decision makers to compare with their domain
knowledge for validation and justify their decisions (cid:2)50(cid:3). In addition, there are a few
advantages of using decision trees over using other data mining algorithms, for instances,
decisiontreesarequicktobuildandeasytointerpretandpredictionbasedondecisiontrees
isefficient(cid:2)22(cid:3).
Decisiontreesarebuiltthroughrecursivedatapartitioning,whereineachiterationthe
dataissplitaccordingtothevaluesofaselectedattribute.Therecursionstopsat“pure”data
subsets which only include instances of the same class (cid:2)53(cid:3). In other words, the principle
idea of a decision tree is to split your data recursively into subsets so that each subset
containsmoreorlesshomogeneousstatesofyourtargetvariable(cid:4)predictableattribute(cid:5).At
each split in the tree, all input attributes are evaluated for their impact on the predictable
attribute. When this recursive process is completed, a decision tree is formed (cid:2)22(cid:3). If the
predictabletargetattributeconsistsofdiscretedata,theformeddecisiontreemodeliscalled
aclassificationtree.However,ifthetargetattributeisacontinuousvariable,thenthemodelis
calledasaregressiontree.Theprocessofdecisiontreebuildingissometimescalleddecision
treeinduction.Manytechniqueshavebeendevelopedfordecisiontreeinductionuptonow.
Nevertheless,thegeneralideaofdecisiontreeinductionissameoneverytypeofdecisiontree
methods.Eachtechniqueemploysalearningalgorithmtoidentifyamodelthatbestfitsthe
relationshipsbetweentheattributesetandclasslabelofinputdata.Themodelgeneratedby
alearningalgorithmshouldbothfittheinputdatawellandcorrectlypredicttheclasslabels
ofrecordsithasneverseenbefore(cid:2)51(cid:3).Anexampleshowingthegenerationofadecisiontree
byemployingtraindataandthepredictionabilitybyusingthetestdataisgiveninFigure3.
ID3 is a well-known decision tree algorithm proposed by Ross Quinlan of the
University of Sydney, Australia. ID3 tree was later enhanced to be C4.5. C4.5 can handle
numeric attributes, missing values, and noisy data. Some decision trees can perform
regression tasks, for example, to predict continuous variables such as temperature and
humidity.TheClassificationandRegressionTree(cid:4)CART(cid:5)proposedbyBriemannisapopular
decisiontreealgorithmforclassificationandregression(cid:2)22(cid:3).
MathematicalProblemsinEngineering 9
5. Landslide Susceptibility Mapping and Results
In order to perform the research reported in the present paper, Microsoft SQL Server 2008
Analysis Services software is chosen as the analyzing platform as it supports decision trees
with continuous variables (cid:4)called as regression trees(cid:5). High scalability and having support
fornestedtable,automaticfeatureselection,automaticcardinalityreductionfeaturesofitare
the other reasons for choosing this data mining platform. Additionally, Microsoft Analysis
ServicesallowsbuildingdataminingapplicationsviathesupportofMicrosoftVisualStudio
Integrated Development Environment and ADOMD extensions (cid:2)22(cid:3). Since the purpose of
this data-mining study is to develop a model for predicting the landslide susceptible areas,
decision tree, a well-known classification technique, has been utilized during the analysis.
Microsoft SQL Server Analysis Services employ their own decision tree algorithm that is
called Microsoft Decision Trees. This commercial algorithm can handle both discrete and
continuous valued variables/attributes and presents useful parameters for configuring tree
induction step such as tree splitting options. Another reason of selecting this algorithm
and software is that it can build dependency network graphs which show the effects
of variables between them and impact degrees of independent variable(cid:4)s(cid:5) on predictable
variable(cid:4)s(cid:5).
In this study, all of the input variables and target output variable are continuous,
so resulted tree is a special version of decision tree named regression tree. Regression is
similartoclassification.Theonlydifferenceisthatregressionpredictscontinuousattributes.
Although the basic task of a decision tree algorithm is classification, it can be used for
regression as well. Another well-known regression tree algorithm is CART. The Microsoft
Decision Trees algorithm adds the support for regression in SQL Server 2005 and 2008.
Microsoft Regression Trees contain a linear regression formula at each leaf node. Using a
regression tree has its advantages over simple linear regression in that a tree can represent
bothlinearandnonlinearrelationships(cid:2)22(cid:3).
Thedataunderstandingandthedatapreparationstagesareamongthemostimportant
steps in the data mining applications (cid:2)54(cid:3). At the start-up of this study, entire dataset
is converted to Access 2003 format from SPSS format to be used in Analysis Services
easily. The dataset used in the analysis consists of 17 input attributes and 1 predictable
attribute (cid:4)landslide; 1 (cid:8) grid cells with landslide, 0 (cid:8) grid cells without landslide(cid:5) and
280,132records/cases (cid:4)Table3(cid:5). 226,041 cases belong to pixel without landslide. However,
53,674casesstandforpixelwithlandslideand417missingcasesexist.Althoughpredictable
attribute (cid:4)landslide information(cid:5) is a discrete valued attribute, it is handled as continious
attribute to obtain more accurate results such as in the range of 0-1 instead of 0 (cid:8) false
and 1 (cid:8) true. After data preparation stage, “Microsoft Decision Trees” algorithm is run on
training dataset by separating whole dataset to 85% training and 15% test cases of whole
dataset. By using the trained model, the landslide susceptibility values of each pixel on the
map are determined via prediction tool of software and the landslide susceptibility map is
produced withthehelpof predicted numerical values ofeach pixelonthemap.Oneofthe
usefulfeaturesof“MicrosoftDecisionTrees”algorithmisbuildingandreportingdependency
networkgraphs.Thedependencynetworkgraphsdisplaytherelationshipsamongattributes
derived from decision tree model’s content (cid:2)22(cid:3). An example view of decision tree that is
derivedfromourmodelispresentedinFigure4.
Using the predicted landslide susceptibility values, the landslide susceptibility map
of the study area is produced (cid:4)Figure5(cid:5). On the map the reddish tones (cid:4)close to 1(cid:5) mean
high susceptible areas while the green parts (cid:4)close to 0(cid:5) represent more stable zones in the
10 MathematicalProblemsinEngineering
All
40<(cid:8)Dem<60 60<Dem<(cid:8)80 140<Dem<(cid:8)160
··· Teoi ··· Teoi ··· Slope ···
Teoi(cid:8)>0.1 Teoi<0.1 Teoi(cid:8)>0.1 Teoi<0.1 Slope(cid:8)>5.795 Slope<5.795
··· Dem Tek ·· Slope Teoiy ·· Teoi Slope ···
:
:
If(cid:4)Dem>(cid:8)40andDem<60(cid:5)
Landslide(cid:8)0.253−0.335∗(cid:4)Tek−0.009(cid:5)(cid:9)0.023∗(cid:4)Slope−5.602(cid:5)−0.356∗(cid:4)Teoiy−0.006(cid:5)(cid:9)
0.214∗(cid:4)Toc−0.104(cid:5)(cid:9)0.167∗(cid:4)Tmb−0.083(cid:5)(cid:9)0.061∗(cid:4)Qa−0.057(cid:5)−0.003∗(cid:4)Dem−49.315(cid:5)(cid:9)
0.369∗(cid:4)Toda−0.187(cid:5)−0.108∗(cid:4)Teoi−0.139(cid:5)(cid:9)0.213∗(cid:4)Tme−0.196(cid:5)(cid:9)0.03∗(cid:4)Spi−0.609(cid:5)
:
If(cid:4)Dem>(cid:8)40andDem<60andTeoi<1(cid:5)
Landslide(cid:8)0.293(cid:9)0.027∗(cid:4)Slope−5.657(cid:5)−0.003∗(cid:4)Dem−49.237(cid:5)(cid:9)0.363∗(cid:4)Toda−0.218(cid:5)(cid:9)
0.068∗(cid:4)Qa−0.066(cid:5)−0.380∗(cid:4)Tek−0.01(cid:5)(cid:9)0.211∗(cid:4)Tme−0.227(cid:5)(cid:9)0.034∗(cid:4)Spi−0.619(cid:5)(cid:9)0.173∗
(cid:4)Tmb−0.097(cid:5)−0.403∗(cid:4)Teoiy−0.007(cid:5)(cid:9)0.205∗(cid:4)Toc−0.121(cid:5)
:
:
Figure4:Anexampleviewofapartofthedecisiontree.
Table3:Theattributesconsideredinthestudyandtheeffectimportanceorderonthepredictablevariable.
Effectimportance
Attribute Continues/discrete Usage
orderontheoutput
Altitude Continuous Input 12
Heatloadindex ” ” 14
Plancurvature ” ” 15
Profilecurvature ” ” 5
Streampowerindex ” ” 3
Slopegradient(cid:4)◦(cid:5) ” ” 4
Alluvium(cid:4)Qa(cid:5) DiscreteinnaturehandledContinuous ” 10
Kirklarelilimestone(Tek) ” ” 2
Ihsaniyefm.(cid:4)Teoi(cid:5) ” ” 6
Ihsaniyefm.Tuffm.(cid:4)Teoi2(cid:5) ” ” 13
Yassiorenlimestone(cid:4)Teoiy(cid:5) ” ” 17
Bakirkoyfm.(cid:4)Tmb(cid:5) ” ” 16
Ergene(cid:4)Tme(cid:5) ” ” 8
Cantakoyfm.(cid:4)Toc(cid:5) ” ” 9
Danismentfm.(cid:4)Tod(cid:5) ” ” 11
Danismentfm.Acmalarm.(Toda) ” ” 1
Suloglufm.(cid:4)Tos(cid:5) ” ” 7
Landslide ” Output