Artificial Intelligence Oceanography

351 Pages·2023·15.492 MB·English

by  Xiaofeng Li, Fan Wang

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
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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