A comparison of ensemble and deep learning algorithms to model groundwater levels in a data-scarce aquifer of Southern Africa

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dc.contributor.authorGaffoor, Zaheed
dc.contributor.authorPietersen, Kevin
dc.contributor.authorJovanovic, Nebo
dc.date.accessioned2022-08-30T13:00:46Z
dc.date.available2022-08-30T13:00:46Z
dc.date.issued2022
dc.description.abstractMachine learning and deep learning have demonstrated usefulness in modelling various groundwater phenomena. However, these techniques require large amounts of data to develop reliable models. In the Southern African Development Community, groundwater datasets are generally poorly developed. Hence, the question arises as to whether machine learning can be a reliable tool to support groundwater management in the data-scarce environments of Southern Africa. This study tests two machine learning algorithms, a gradient-boosted decision tree (GBDT) and a long short-term memory neural network (LSTM-NN), to model groundwater level (GWL) changes in the Shire Valley Alluvial Aquifer.en_US
dc.identifier.citationGaffoor, Z. et al. (2022). A comparison of ensemble and deep learning algorithms to model groundwater levels in a data-scarce aquifer of Southern Africa. Hydrology, 9(7), 25. https://doi.org/10.3390/hydrology9070125en_US
dc.identifier.issn2306-5338
dc.identifier.urihttps://doi.org/10.3390/hydrology9070125
dc.identifier.urihttp://hdl.handle.net/10566/7785
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.subjectGroundwateren_US
dc.subjectNeural networksen_US
dc.subjectMachine learningen_US
dc.subjectSouth Africaen_US
dc.subjectEarth scienceen_US
dc.titleA comparison of ensemble and deep learning algorithms to model groundwater levels in a data-scarce aquifer of Southern Africaen_US
dc.typeArticleen_US

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