03396nam 2201033z- 450 991055744810332120231214133032.0(CKB)5400000000043259(oapen)https://directory.doabooks.org/handle/20.500.12854/76675(EXLCZ)99540000000004325920202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierMachine Learning with Metaheuristic Algorithms for Sustainable Water Resources ManagementBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 electronic resource (238 p.)3-0365-1720-0 3-0365-1719-7 The 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.Research & information: generalbicsscgroundwaterartificial intelligencehydrologic modelgroundwater level predictionmachine learningprincipal component analysisspatiotemporal variationuncertainty analysishydroinformaticssupport vector machinebig dataartificial neural networknitrogen compoundnitrogen predictionprediction modelsneural networknon-linear modelingPACFWANNSVM-LFSVM-RFGovindpurstreamflow forecastingBayesian model averagingmultivariate adaptive regression splineM5 model treeKernel extreme learning machinesSouth Koreauncertaintysustainabilityprediction intervalsungauged basinstreamflow simulationsatellite precipitationatmospheric reanalysisensemble modelingadditive regressionbaggingdaggingrandom subspacerotation forestflood routingMuskingum methodextension principlecalibrationfuzzy sets and systemsparticle swarm optimizationEEFluxirrigation performanceCWPwater conservationNDVIwater resourcesDaymet V3Google Earth Engineimproved extreme learning machine (IELM)sensitivity analysisshortwave radiation flux densitysustainable developmentResearch & information: generalKisi Ozguredt1323739Kisi OzgurothBOOK9910557448103321Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management3035796UNINA