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Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia

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dc.citedby87
dc.contributor.authorIbrahem Ahmed Osman A.en_US
dc.contributor.authorNajah Ahmed A.en_US
dc.contributor.authorChow M.F.en_US
dc.contributor.authorFeng Huang Y.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57221644207en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57214146115en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:07:32Z
dc.date.available2023-05-29T09:07:32Z
dc.date.issued2021
dc.descriptionForecasting; Groundwater; Learning algorithms; Machine learning; Neural networks; Rain; Support vector regression; Gradient boosting; Machine learning models; Malaysia; Prediction model; Rainfall data; Support vector regression models; Predictive analyticsen_US
dc.description.abstractGroundwater levels have been declining recently in Malaysia. This is why, the current study was aimed to propose an accurate groundwater levels prediction model using machine learning algorithms in highly populated towns in Selangor, Malaysia. The models developed used 11 months of previously recorded data of rainfall, temperature and evaporation to predict groundwater levels. Three machine learning models have been tested and evaluated; Xgboost, Artificial Neural Network, and Support Vector Regression. The results showed that for the first scenario, which had combinations of 1,2 and 3 days delayed of rainfall data only considered as an input, the models� performance was the worst. while in the second scenario the proposed Xgboost model outperformed both the Artificial Neural Network and Support Vector Regression models for all different input combinations. A significant increase in performance was achieved in the third scenario, when using 1 day delayed of groundwater levels as an input as well where R2 equal to 0.92 in the Xgboost model in scenario 3 and 0.16, 0.11 in scenarios 2 and 1 respectively. The results obtained in this study serves as a great benchmark for future groundwater levels prediction using Xgboost algorithm. � 2020 Faculty of Engineering, Ain Shams Universityen_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.asej.2020.11.011
dc.identifier.epage1556
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85099701203
dc.identifier.spage1545
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85099701203&doi=10.1016%2fj.asej.2020.11.011&partnerID=40&md5=825296cae0eba3d475d8a90b51c07a46
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26186
dc.identifier.volume12
dc.publisherAin Shams Universityen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleAin Shams Engineering Journal
dc.titleExtreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysiaen_US
dc.typeArticleen_US
dspace.entity.typePublication
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