Comparison of machine learning algorithms for emulation of a gridded hydrological model given spatially explicit inputs

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dc.contributor.authorLim, Theodore C.en
dc.contributor.authorWang, Kaidien
dc.date.accessioned2022-01-22T18:57:53Zen
dc.date.available2022-01-22T18:57:53Zen
dc.date.issued2022-02-01en
dc.date.updated2022-01-22T18:57:51Zen
dc.description.abstractThis study compares the performance of several machine learning algorithms in reproducing the spatial and temporal outputs of the process-based, hydrological model, ParFlow.CLM. Emulators or surrogate models are often used to reduce complexity and simulation times of complex models, and have typically been applied to evaluate parameter sensitivity or for model parameter tuning, without explicit treatment of variation resulting from spatially explicit inputs to the model. Here we present a case study in which we evaluate candidate machine learning algorithms for suitability emulating model outputs given spatially explicit inputs. We find that among random forest, gaussian process, k-nearest neighbors, and deep neural networks, the random forest algorithm performs the best on small training sets, is not as sensitive to hyperparameters chosen for the machine learning model, and can be trained quickly. Although deep neural networks were hypothesized to be able to better capture the potential nonlinear interactions in ParFlow.CLM, they also required more training data and much more refined tuning of hyperparameters to achieve the potential benefits of the algorithm.en
dc.description.versionAccepted versionen
dc.format.extentPages 105025-105025en
dc.format.mimetypeapplication/pdfen
dc.identifier105025 (Article number)en
dc.identifier.doihttps://doi.org/10.1016/j.cageo.2021.105025en
dc.identifier.issn0098-3004en
dc.identifier.orcidLim, Theodore [0000-0002-7896-4964]en
dc.identifier.urihttp://hdl.handle.net/10919/107855en
dc.identifier.volume159en
dc.language.isoenen
dc.publisherElsevieren
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject04 Earth Sciencesen
dc.subject08 Information and Computing Sciencesen
dc.subject09 Engineeringen
dc.subjectGeochemistry & Geophysicsen
dc.titleComparison of machine learning algorithms for emulation of a gridded hydrological model given spatially explicit inputsen
dc.title.serialComputers and Geosciencesen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherJournal Articleen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Architecture and Urban Studiesen
pubs.organisational-group/Virginia Tech/Architecture and Urban Studies/School of Public and International Affairsen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Architecture and Urban Studies/CAUS T&R Facultyen

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