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dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorWanders, Niko
dc.contributor.authorVen, Jordy van de
dc.date.accessioned2022-04-07T00:00:37Z
dc.date.available2022-04-07T00:00:37Z
dc.date.issued2022
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/41461
dc.description.abstractHydrological droughts can have severe impacts on water levels in a river and consequentially also on shipping. Traditionally research on the impact of hydrological drought is done by means of numerical modeling. In this study a machine learning approach was used, to investigate the viability of data driven approaches in drought estimations. It was found that random forest machine learning is a promising tool that can be used to study the impact of hydrological drought.
dc.description.sponsorshipUtrecht University
dc.language.isoEN
dc.subjectA random forest machine learning program developed to estimate the impact of hydrological drought on the shipping industry in the Netherlands.
dc.titleUsing Random Forest Machine learning to estimate the impact of hydrological drought on the shipping industry
dc.type.contentMaster Thesis
dc.rights.accessrightsOpen Access
dc.subject.keywordsDrought;Machine learning:random forest;shipping
dc.subject.courseuuEarth Surface and Water
dc.thesis.id3233


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