Unveiling key drivers in hydrometeorological processes feature selection using bayesian networks for improved understanding and prediction

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dc.contributor.authorDas, Prabal
dc.date.accessioned2024-02-20T11:03:08Z
dc.date.available2024-02-20T11:03:08Z
dc.date.issued2024-01
dc.identifier.urihttp://hdl.handle.net/123456789/3003
dc.language.isoenen_US
dc.publisherIndian Institute of Technology(Indian School of Mines)Dhanbaden_US
dc.subjectBayesian Networks (BNs)en_US
dc.subjectMachine Learning (ML)en_US
dc.subjectArtificial Neural Networksen_US
dc.subjectSupport Vector Regression (SVR)en_US
dc.subjectRecursive Feature Elimination (RFE)en_US
dc.subjectGeneral Circulation Models (GCMs)en_US
dc.subjectPH2683en_US
dc.subjectCIVen_US
dc.subjectPh.Den_US
dc.titleUnveiling key drivers in hydrometeorological processes feature selection using bayesian networks for improved understanding and predictionen_US
dc.typeThesisen_US
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