Petascale analytics : large-scale machine learning in the earth sciences

238 Pages·2016·21.55 MB·English

by  Ashok N. Srivastava

Large-Scale Machine Learning
in the Earth Sciences
Chapman & Hall/CRC
Data Mining and Knowledge Discovery Series
Series Editor: Vipin Kumar
UniversityofMinnesota
DepartmentofComputerScienceandEngineering
Minneapolis,Minnesota,U.S.A.
AIMSANDSCOPE
Thisseriesaimstocapturenewdevelopmentsandapplicationsindataminingandknowledge
discovery,whilesummarizingthecomputationaltoolsandtechniquesusefulindataanalysis.
Thisseriesencouragestheintegrationofmathematical,statistical,andcomputationalmethods
andtechniquesthroughthepublicationofabroadrangeoftextbooks,referenceworks,and
handbooks.Theinclusionofconcreteexamplesandapplicationsishighlyencouraged.The
scopeoftheseriesincludes,butisnotlimitedto,titlesintheareasofdataminingand
knowledgediscoverymethodsandapplications,modeling,algorithms,theoryandfoundations,
dataandknowledgevisualization,dataminingsystemsandtools,andprivacyandsecurity
issues.
PUBLISHEDTITLES
AcceleratingDiscovery:MiningUnstructuredInformationforHypothesisGeneration
ScottSpangler
AdvancesinMachineLearningandDataMiningforAstronomy
MichaelJ.Way,JeffreyD.Scargle,KamalM.Ali,andAshokN.Srivastava
BiologicalDataMining
JakeY.ChenandStefanoLonardi
ComputationalBusinessAnalytics
SubrataDas
ComputationalIntelligentDataAnalysisforSustainableDevelopment
TingYu,NiteshV.Chawla,andSimeonSimoff
ComputationalMethodsofFeatureSelection
HuanLiuandHiroshiMotoda
ConstrainedClustering:AdvancesinAlgorithms,Theory,andApplications
SugatoBasu,IanDavidson,andKiriL.Wagstaff
ContrastDataMining:Concepts,Algorithms,andApplications
GuozhuDongandJamesBailey
DataClassification:AlgorithmsandApplications
CharuC.Aggarawal
DataClustering:AlgorithmsandApplications
CharuC.AggarawalandChandanK.Reddy
DataClusteringinC++:AnObject-OrientedApproach
GuojunGan
DataMining:ATutorial-BasedPrimer,SecondEdition
RichardJ.Roiger
DataMiningforDesignandMarketing
YukioOhsawaandKatsutoshiYada
DataMiningwithR:LearningwithCaseStudies,SecondEdition
LuísTorgo
DataScienceandAnalyticswithPython
JesusRogel-Salazar
EventMining:AlgorithmsandApplications
TaoLi
FoundationsofPredictiveAnalytics
JamesWuandStephenCoggeshall
GeographicDataMiningandKnowledgeDiscovery,SecondEdition
HarveyJ.MillerandJiaweiHan
Graph-BasedSocialMediaAnalysis
IoannisPitas
HandbookofEducationalDataMining
CristóbalRomero,SebastianVentura,MykolaPechenizkiy,andRyanS.J.d.Baker
HealthcareDataAnalytics
ChandanK.ReddyandCharuC.Aggarwal
InformationDiscoveryonElectronicHealthRecords
VagelisHristidis
IntelligentTechnologiesforWebApplications
PritiSrinivasSajjaandRajendraAkerkar
IntroductiontoPrivacy-PreservingDataPublishing:ConceptsandTechniques
BenjaminC.M.Fung,KeWang,AdaWai-CheeFu,andPhilipS.Yu
KnowledgeDiscoveryforCounterterrorismandLawEnforcement
DavidSkillicorn
KnowledgeDiscoveryfromDataStreams
JoãoGama
Large-ScaleMachineLearninginTheEarthSciences
AshokN.Srivastava,RamakrishnaNemani,andKarstenSteinhaeuser
MachineLearningandKnowledgeDiscoveryforEngineeringSystemsHealthManagement
AshokN.SrivastavaandJiaweiHan
MiningSoftwareSpecifications:MethodologiesandApplications
DavidLo,Siau-ChengKhoo,JiaweiHan,andChaoLiu
MultimediaDataMining:ASystematicIntroductiontoConceptsandTheory
ZhongfeiZhangandRuofeiZhang
MusicDataMining
TaoLi,MitsunoriOgihara,andGeorgeTzanetakis
NextGenerationofDataMining
HillolKargupta,JiaweiHan,PhilipS.Yu,RajeevMotwani,andVipinKumar
Rapidminer:DataMiningUseCasesandBusinessAnalyticsApplications
MarkusHofmannandRalfKlinkenberg
RelationalDataClustering:Models,Algorithms,andApplications
BoLong,ZhongfeiZhang,andPhilipS.Yu
Service-OrientedDistributedKnowledgeDiscovery
DomenicoTaliaandPaoloTrunfio
SpectralFeatureSelectionForDataMining
ZhengAlanZhaoandHuanLiu
StatisticalDataMiningUsingSASApplications,SecondEdition
GeorgeFernandez
SupportVectorMachines:OptimizationBasedTheory,Algorithms,andExtensions
NaiyangDeng,YingjieTian,andChunhuaZhang
TemporalDataMining
TheophanoMitsa
TextMining:Classification,Clustering,andApplications
AshokN.SrivastavaandMehranSahami
TextMiningandVisualization:CaseStudiesUsingOpen-SourceTools
MarkusHofmannandAndrewChisholm
TheTopTenAlgorithmsinDataMining
XindongWuandVipinKumar
UnderstandingComplexDatasets:DataMiningwithMatrixDecompositions
DavidSkillicorn
Large-Scale Machine Learning
in the Earth Sciences
Editedby
Ashok N. Srivastava
Ramakrishna Nemani
Karsten Steinhaeuser
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LibraryofCongressCataloging-in-PublicationData
Names:Srivastava,AshokN.(AshokNarain),1969-editor.|Nemani,
Ramakrishna,editor.|Steinhaeuser,Karsten,editor.
Title:Large-scalemachinelearningintheearthsciences/[editedby]Ashok
N.Srivastava,Dr.RamakrishnaNemani,KarstenSteinhaeuser.
Description:BocaRaton:Taylor&Francis,2017.|Series:Chapman&
Hall/CRCdatamining&knowledgediscoveryseries;42|“ACRCtitle,
partoftheTaylor&Francisimprint,amemberoftheTaylor&Francis
Group,theacademicdivisionofT&FInformaplc.”
Identifiers:LCCN2017006160|ISBN9781498703871(hardback:alk.paper)
Subjects:LCSH:Earthsciences–Computernetworkresources.|Earth
sciences–Dataprocessing.
Classification:LCCQE48.87.L372017|DDC550.285/6312–dc23
LCrecordavailableathttps://lccn.loc.gov/2017006160
VisittheTaylor&FrancisWebsiteat
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Contents
Foreword.............................................................................................................. ix
Editors................................................................................................................. xi
Contributors........................................................................................................xiii
Introduction .........................................................................................................xv
1 NetworkSciencePerspectivesonEngineeringAdaptationtoClimateChangeand
WeatherExtremes ........................................................................................... 1
UditBhatiaandAuroopR.Ganguly
2 StructuredEstimationinHighDimensions:ApplicationsinClimate.....................13
AndréRGoncalves,ArindamBanerjee,VidyashankarSivakumar,andSoumyadeepChatterjee
3 SpatiotemporalGlobalClimateModelTracking .................................................33
ScottMcQuadeandClaireMonteleoni
4 StatisticalDownscalinginClimatewithState-of-the-ArtScalableMachine
Learning........................................................................................................55
ThomasVandal,UditBhatia,andAuroopR.Ganguly
5 Large-ScaleMachineLearningforSpeciesDistributions......................................73
ReidA.Johnson,JasonD.K.Dzurisin,andNiteshV.Chawla
6 UsingLarge-ScaleMachineLearningtoImproveOurUnderstandingofthe
FormationofTornadoes..................................................................................95
AmyMcGovern,CoreyPotvin,andRodgerA.Brown
7 DeepLearningforVeryHigh-ResolutionImageryClassification........................113
SangramGanguly,SaikatBasu,RamakrishnaNemani,SupratikMukhopadhyay,Andrew
Michaelis,PetrVotava,CristinaMilesi,andUttamKumar
vii
viii Contents
8 UnmixingAlgorithms:AReviewofTechniquesforSpectralDetectionand
ClassificationofLandCoverfromMixedPixelsonNASAEarthExchange ..........131
UttamKumar,CristinaMilesi,S.KumarRaja,RamakrishnaNemani,SangramGanguly,
WeileWang,PetrVotava,AndrewMichaelis,andSaikatBasu
9 SemanticInteroperabilityofLong-TailGeoscienceResourcesovertheWeb.........175
MostafaM.Elag,PraveenKumar,LuigiMarini,ScottD.Peckham,andRuiLiu
Index ......................................................................................................201
Foreword
The climate and Earth sciences have recently undergone a rapid transformation from a data-poor to a
data-richenvironment.Inparticular,massiveamountsofclimateandecosystemdataarenowavailable
fromsatelliteandground-basedsensors,andphysics-basedclimatemodelsimulations.Theseinformation-
richdatasetsofferhugepotentialformonitoring,understanding,andpredictingthebehavioroftheEarth’s
ecosystemandforadvancingthescienceofglobalchange.
Whilelarge-scalemachinelearninganddatamininghavegreatlyimpactedarangeofcommercialappli-
cations, their use in the field of Earth sciences is still in the early stages. This book, edited by Ashok
Srivastava,RamakrishnaNemani,andKarstenSteinhaeuser,servesasanoutstandingresourceforany-
oneinterestedintheopportunitiesandchallengesforthemachinelearningcommunityinanalyzingthese
datasetstoanswerquestionsofurgentsocietalinterest.
ThisbookisacompilationofrecentresearchintheapplicationofmachinelearninginthefieldofEarth
sciences.Itdiscussesanumberofapplicationsthatexemplifysomeofthemostimportantquestionsfaced
bytheclimateandecosystemscientiststodayandtherolethedataminingcommunitycanplayinanswer-
ingthem.Chaptersarewrittenbyexpertswhoareworkingattheintersectionofthetwofields.Topics
covered include modeling of weather and climate extremes, evaluation of climate models, and the use
of remote sensing data to quantify land-cover change dynamics. Collectively, they provide an excellent
cross-sectionofresearchbeingdoneinthisemergingfieldofgreatsocietalimportance.
Ihopethatthisbookwillinspiremorecomputerscientiststofocusonenvironmentalapplications,and
Earthscientiststoseekcollaborationswithresearchersinmachinelearninganddataminingtoadvance
thefrontiersinEarthsciences.
VipinKumar,PhD
DepartmentofComputerScienceandEngineering
UniversityofMinnesota
Minneapolis,MN
ix