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Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management

dc.contributor.editorKisi, Ozgur
dc.date.accessioned2022-01-11T13:38:46Z
dc.date.available2022-01-11T13:38:46Z
dc.date.issued2021
dc.identifierONIX_20220111_9783036517209_410
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/76675
dc.description.abstractThe main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management.
dc.languageEnglish
dc.subject.classificationthema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: generalen_US
dc.subject.othergroundwater
dc.subject.otherartificial intelligence
dc.subject.otherhydrologic model
dc.subject.othergroundwater level prediction
dc.subject.othermachine learning
dc.subject.otherprincipal component analysis
dc.subject.otherspatiotemporal variation
dc.subject.otheruncertainty analysis
dc.subject.otherhydroinformatics
dc.subject.othersupport vector machine
dc.subject.otherbig data
dc.subject.otherartificial neural network
dc.subject.othernitrogen compound
dc.subject.othernitrogen prediction
dc.subject.otherprediction models
dc.subject.otherneural network
dc.subject.othernon-linear modeling
dc.subject.otherPACF
dc.subject.otherWANN
dc.subject.otherSVM-LF
dc.subject.otherSVM-RF
dc.subject.otherGovindpur
dc.subject.otherstreamflow forecasting
dc.subject.otherBayesian model averaging
dc.subject.othermultivariate adaptive regression spline
dc.subject.otherM5 model tree
dc.subject.otherKernel extreme learning machines
dc.subject.otherSouth Korea
dc.subject.otheruncertainty
dc.subject.othersustainability
dc.subject.otherprediction intervals
dc.subject.otherungauged basin
dc.subject.otherstreamflow simulation
dc.subject.othersatellite precipitation
dc.subject.otheratmospheric reanalysis
dc.subject.otherensemble modeling
dc.subject.otheradditive regression
dc.subject.otherbagging
dc.subject.otherdagging
dc.subject.otherrandom subspace
dc.subject.otherrotation forest
dc.subject.otherflood routing
dc.subject.otherMuskingum method
dc.subject.otherextension principle
dc.subject.othercalibration
dc.subject.otherfuzzy sets and systems
dc.subject.otherparticle swarm optimization
dc.subject.otherEEFlux
dc.subject.otherirrigation performance
dc.subject.otherCWP
dc.subject.otherwater conservation
dc.subject.otherNDVI
dc.subject.otherwater resources
dc.subject.otherDaymet V3
dc.subject.otherGoogle Earth Engine
dc.subject.otherimproved extreme learning machine (IELM)
dc.subject.othersensitivity analysis
dc.subject.othershortwave radiation flux density
dc.subject.othersustainable development
dc.subject.othern/a
dc.titleMachine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management
dc.typebook
oapen.identifier.doi10.3390/books978-3-0365-1719-3
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0
oapen.relation.isbn9783036517209
oapen.relation.isbn9783036517193
oapen.pages238
oapen.place.publicationBasel, Switzerland


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