Machine learning based groundwater prediction in a data-scarce basin of Ghana

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cg.contributor.affiliationInternational Water Management Instituteen
cg.coverage.countryGhana
cg.coverage.iso3166-alpha2GH
cg.coverage.regionWestern Africa
cg.coverage.subregionVolta Basin
cg.coverage.subregionAkuse
cg.coverage.subregionKetekrachi
cg.coverage.subregionTamale
cg.coverage.subregionWenchi
cg.creator.identifierDr Akpoti Komlavi: 0000-0001-6435-5116
cg.identifier.doihttps://doi.org/10.1080/08839514.2022.2138130en
cg.identifier.iwmilibraryH051547
cg.isijournalISI Journalen
cg.issn0883-9514en
cg.issue1en
cg.journalApplied Artificial Intelligenceen
cg.reviewStatusPeer Reviewen
cg.river.basinVOLTAen
cg.volume36en
dc.contributor.authorSiabi, E. K.en
dc.contributor.authorDile, Y. T.en
dc.contributor.authorKabo-Bah, A. T.en
dc.contributor.authorAmo-Boateng, M.en
dc.contributor.authorAnornu, G. K.en
dc.contributor.authorAkpoti, Komlavien
dc.contributor.authorVuu, C.en
dc.contributor.authorDonkor, P.en
dc.contributor.authorMensah, S. K.en
dc.contributor.authorIncoom, A. B. M.en
dc.contributor.authorOpoku, E. K.en
dc.contributor.authorAtta-Darkwa, T.en
dc.date.accessioned2022-11-29T11:27:31Zen
dc.date.available2022-11-29T11:27:31Zen
dc.identifier.urihttps://hdl.handle.net/10568/125697
dc.titleMachine learning based groundwater prediction in a data-scarce basin of Ghanaen
dcterms.abstractGroundwater (GW) is a key source of drinking water and irrigation to combat growing food insecurity and for improved water access in rural sub-Saharan Africa. However, there are limited studies due to data scarcity in the region. New modeling techniques such as Machine learning (ML) are found robust and promising tools to assess GW recharge with less expensive data. The study utilized ML technique in GW recharge prediction for selected locations to assess sustainability of GW resources in Ghana. Two artificial neural networks (ANN) models namely Feedforward Neural Network with Multilayer Perceptron (FNN-MLP) and Extreme Learning Machine (FNN-ELM) were used for the prediction of GW using 58 years (1960–2018) of GW data. Model evaluation between FNN-MLP and FNN-ELM showed that the former approach was better in predicting GW with R2 ranging from 0.97 to 0.99 while the latter has an R2 between 0.42 to 0.68. The overall performance of both models was acceptable and suggests that ANN is a useful forecasting tool for GW assessment. The outcomes from this study will add value to the current methods of GW assessment and development, which is one of the pillars of the sustainable development goals (SDG 6).en
dcterms.accessRightsOpen Access
dcterms.available2022-10-30
dcterms.bibliographicCitationSiabi, E. K.; Dile, Y. T.; Kabo-Bah, A. T.; Amo-Boateng, M.; Anornu, G. K.; Akpoti, Komlavi; Vuu, C.; Donkor, P.; Mensah, S. K.; Incoom, A. B. M.; Opoku, E. K.; Atta-Darkwa, T. 2022. Machine learning based groundwater prediction in a data-scarce basin of Ghana. Applied Artificial Intelligence, 36(1):2138130. [doi: https://doi.org/10.1080/08839514.2022.2138130]en
dcterms.extent2138130en
dcterms.issued2022-12-31
dcterms.languageen
dcterms.licenseCC-BY-NC-4.0
dcterms.publisherInforma UK Limiteden
dcterms.subjectgroundwater rechargeen
dcterms.subjectforecastingen
dcterms.subjectestimationen
dcterms.subjectmachine learningen
dcterms.subjectneural networksen
dcterms.subjectmodellingen
dcterms.subjectprecipitationen
dcterms.subjectevapotranspirationen
dcterms.subjectsurface runoffen
dcterms.subjectclimate changeen
dcterms.subjectrainen
dcterms.subjectaquifersen
dcterms.typeJournal Article

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