Artificial Intelligence Oceanography PDF
Preview Artificial Intelligence Oceanography
Xiaofeng Li · Fan Wang Editors Artificial Intelligence Oceanography Artificial Intelligence Oceanography · Xiaofeng Li Fan Wang Editors Artificial Intelligence Oceanography Editors XiaofengLi FanWang CASKeyLaboratoryofOceanCirculation CASKeyLaboratoryofOceanCirculation andWaves andWaves InstituteofOceanology,ChineseAcademy InstituteofOceanology,ChineseAcademy ofSciences ofSciences Qingdao,Shandong,China Qingdao,Shandong,China ISBN 978-981-19-6374-2 ISBN 978-981-19-6375-9 (eBook) https://doi.org/10.1007/978-981-19-6375-9 ©TheEditor(s)(ifapplicable)andTheAuthor(s)2023.Thisbookisanopenaccesspublication. Open Access This book is licensed under the terms of the Creative Commons Attribution- NonCommercial-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nc- nd/4.0/),whichpermitsanynoncommercialuse,sharing,distributionandreproductioninanymediumor format,aslongasyougiveappropriatecredittotheoriginalauthor(s)andthesource,providealinktothe CreativeCommonslicenseandindicateifyoumodifiedthelicensedmaterial.Youdonothavepermission underthislicensetoshareadaptedmaterialderivedfromthisbookorpartsofit. Theimagesorotherthirdpartymaterialinthisbookareincludedinthebook’sCreativeCommonslicense, unlessindicatedotherwiseinacreditlinetothematerial.Ifmaterialisnotincludedinthebook’sCreative Commonslicenseandyourintendeduseisnotpermittedbystatutoryregulationorexceedsthepermitted use,youwillneedtoobtainpermissiondirectlyfromthecopyrightholder. Thisworkissubjecttocopyright.Allcommercialrightsarereservedbytheauthor(s),whetherthewhole orpartofthematerialisconcerned,specificallytherightsoftranslation,reprinting,reuseofillustrations, recitation,broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmissionor informationstorageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilar methodologynowknownorhereafterdeveloped.Regardingthesecommercialrightsanon-exclusive licensehasbeengrantedtothepublisher. Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant protectivelawsandregulationsandthereforefreeforgeneraluse. Thepublisher,theauthors,andtheeditorsaresafetoassumethattheadviceandinformationinthisbook arebelievedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsor theeditorsgiveawarranty,expressedorimplied,withrespecttothematerialcontainedhereinorforany errorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictional claimsinpublishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSingaporePteLtd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore Preface The ocean accounts for about 71% of Earth’s surface, yet many aspects remain a mystery. Understanding ocean circulation, biogeochemical cycles, and various marineresourcesdirectlyimpacthumanactivitiesinthe21stcentury.Theenhanced utilization of satellites and autonomous observation platforms has acquired in-situ andremotelysenseddataathighspatialandtemporalresolutionsforthepastfour decades, entering the Big Ocean Data Era. However, the human capacity to filter, curate,andanalyzethesedataislimited.Intheeraofbigdata,efficientlyobtaining helpfulinformationfrommassivedatahasbecomeanewchallengeinoceanographic research. Artificialintelligencetechnologyhasbeenubiquitouslyappliedacrossscientific domains and disciplines and achieved tremendous success. For example, machine learningapproacheshavebeenwidelyusedincomputervision,medical,orgeophys- icalfields.Machine learningisanapplication ofartificialintelligence thataimsto providesystemstolearnfromexperiencewithouthumaninterventionautomatically. Withtherapidincreaseincomputingpowerinrecentyears,deeplearning,amore advancedmachinelearningtechnologyhasbeguntoshowitspowersinsolvingvery complex,nonlinear,high-dimensionalproblems.Promisingly,theseartificialintelli- genceapproachesalsohaveenormouspotentialtoimprovethequalityandextentof oceanresearchbyidentifyinglatentpatternsandhiddentrends,particularlyinlarge datasets that are intractable using other traditional methods. In addition, the new data-drivenandlearning-basedmethodologiesmayproposenovelcomputationally efficientstrategiestoimproveoceanographicresearch. v vi Preface This book brings together state-of-the-art studies on the broad theme of artifi- cialintelligenceapplicationsinoceanography,includingpuredata-drivenforecasts, datasetreconstruction,anddetectionorextractionofoceanicfeaturesfromremote sensing imagery. The comprehensive contributions clarify the tremendous poten- tial for artificial intelligence technology to contribute to rapid advances in ocean scienceandmayinspirereadersofrelateddisciplines.Astheeditorsofthisbook,we wouldliketothankallthecontributorsfortheirfruitfulcooperationandtheeditorial assistancefromDr.ShuangshangZhang. Qingdao,China XiaofengLi December2021 FanWang Contents ArtificialIntelligenceFoundationofSmartOcean .................... 1 XiaofengLi,FanWang,YuanZhou,andKeranChen Forecasting Tropical Instability Waves Based on Artificial Intelligence ....................................................... 45 GangZheng,XiaofengLi,RonghuaZhang,andBinLiu Sea Surface Height Anomaly Prediction Based on Artificial Intelligence ....................................................... 63 YuanZhou,ChangLu,KeranChen,andXiaofengLi Satellite Data-Driven Internal Solitary Wave Forecast Based onMachineLearningTechniques ................................... 83 XudongZhang,QuananZheng,andXiaofengLi AI-Based Subsurface Thermohaline Structure Retrieval fromRemoteSensingObservations ................................. 105 HuaSu,WenfangLu,AnWang,andTianyiZhang OceanHeatContentRetrievalfromRemoteSensingDataBased onMachineLearning .............................................. 125 WenfangLuandHuaSu DetectingTropicalCyclogenesisUsingBroadLearningSystem fromSatellitePassiveMicrowaveObservations ....................... 147 ShengWangandXiaofengYang TropicalCycloneMonitoringBasedonGeostationarySatellite Imagery .......................................................... 165 ChongWang,QingXu,XiaofengLi,GangZheng,andBinLiu Reconstruction of pCO Data in the Southern Ocean Based 2 onFeedforwardNeuralNetwork .................................... 189 YanjunWang, XiaofengLi, JinmingSong, XuegangLi, GuorongZhong,andBinZhang vii viii Contents Detection and Analysis of Mesoscale Eddies Based on Deep Learning ......................................................... 209 YingjieLiu,QuananZheng,andXiaofengLi DeepConvolutionalNeuralNetworks-BasedCoastalInundation Mapping from SAR Imagery: with One Application Case forBangladesh,aUN-definedLeastDevelopedCountry ............... 227 BinLiu,XiaofengLi,andGangZheng Sea Ice Detection from SAR Images Based on Deep Fully ConvolutionalNetworks ............................................ 253 YibinRen,XiaofengLi,XiaofengYang,andHuanXu DetectionandAnalysisofMarineGreenAlgaeBasedonArtificial Intelligence ....................................................... 277 LeGao,XiaofengLi,YuanGuo,FanzhouKong,andRenchengYu Automatic Waterline Extraction of Large-Scale Tidal Flats fromSARImagesBasedonDeepConvolutionalNeuralNetworks ..... 287 ShuangshangZhang,QingXu,andXiaofengLi ExtractingShip’sSizefromSARImagesbyDeepLearning ........... 303 YibinRen,XiaofengLi,andHuanXu Benthic Organism Detection, Quantification and Seamount BiologyDetectionBasedonDeepLearning ........................... 323 YuhaiLiu,YuXu,HainingWang,andXiaofengLi Acronyms AC AgulhasCurrent ACC AntarcticCircumpolarCurrent AE AnticyclonicEddy AHI AdvancedHimawariImager AI ArtificialIntelligence ANN ArtificialNeuralNetwork AP AveragePrecision ARC AgulhasReturnCurrent ARKTOS AdvancedReasoningUsingKnowledgeforTypingOfSeaIce AVHRR AdvancedVeryHighResolutionRadiometer AVISO Archiving,Validation,andInterpretationofSatelliteOceanographic Data BCE BinaryCross-Entropy Bi-LSTM Bi-LongShort-TermMemory BLS BroadLearningSystem BN BatchNormalization BP Back-Propagation BRNN BidirectionalCyclicNeuralNetwork,BidirectionalRNN CAM ChannelAttentionModule CE CyclonicEddy CFAR ConstantFalseAlarmRate CNY ChineseYuan CHL ChlorophyllConcentration CMA ChinaMeteorologicalAdministration CMEMS CopernicusMarineEnvironmentMonitoringService CMW CloudMotionWind CNN ConvolutionalNeuralNetwork CUDA ComputeUnifiedDeviceArchitecture DAU-Net Dual-AttentionU-NetModel DBN DeepBeliefNetworks DCNN DeepConvolutionNeuralNetworks ix x Acronyms DEM DigitalElevationModel DL DeepLearning DMSP DefenseMeteorologicalSatelliteProgram DNN DeepNeuralNetwork DORS DeepOceanRemoteSensing DT DvorakTechnique EAC EastAustraliaCurrent EEI Earth’sEnergyImbalance EMD EmpiricalModeDecomposition EMS CopernicusEmergencyManagementService ENSO ElNiñoSouthernOscillation ENVISAT EnvironmentalSatellite EOF EmpiricalOrthogonalFunction ESA EuropeanSpaceAgency FAI FloatingAlgaeIndex FAR FalseAlarmRatio Faster-RCNN FasterRegion-BasedConvolutionalNetwork FCN FullyConnectedNeural FNN FeedforwardNeuralNetwork FPN FeaturePyramidNetwork GAN GenerativeAdversarialNetwork GBDT GradientBoostingDecisionTree GBR GradientBoostingRegression GFO GeosatFollowOn GLCM Gray-LevelCo-OccurrenceMatrix GMT GreenwichMeanTime GPU GraphicProcessingUnit GRD GroundRangeDetected GS GulfStream H-8 Himawari-8 HAB HarmfulAlgalBlooms HED Holistically-NestedEdgeDetectionNetwork HOG HistogramofOrientedGradient HOV HumanOccupiedVehicle HR High-Resolution HT HitRate IA IncidentAngle IBTrACS InternationalBestTrackArchiveforClimateStewardshipDataset IMS IceMappingSystem IoU IntersectionOverUnion IPL InformationProcessingLanguage IR Infrared ISW InternalSolitaryWave IW InterferometricWide-Swath JMA JapanMeteorologicalAgency
The list of books you might like
