Groundwater prediction using machine-learning tools

dc.contributor.authorHussein, Eslam A.
dc.contributor.authorThron, Christopher
dc.contributor.authorGhaziasgar, Mehrdad
dc.date.accessioned2021-01-27T10:16:37Z
dc.date.available2021-01-27T10:16:37Z
dc.date.issued2020
dc.description.abstractPredicting groundwater availability is important to water sustainability and drought mitigation. Machine-learning tools have the potential to improve groundwater prediction, thus enabling resource planners to: (1) anticipate water quality in unsampled areas or depth zones; (2) design targeted monitoring programs; (3) inform groundwater protection strategies; and (4) evaluate the sustainability of groundwater sources of drinking water. This paper proposes a machine-learning approach to groundwater prediction with the following characteristics: (i) the use of a regression-based approach to predict full groundwater images based on sequences of monthly groundwater maps; (ii) strategic automatic feature selection (both local and global features) using extreme gradient boosting; and (iii) the use of a multiplicity of machine-learning techniques (extreme gradient boosting, multivariate linear regression, random forests, multilayer perceptron and support vector regression). Of these techniques, support vector regression consistently performed best in terms of minimizing root mean square error and mean absolute error. Furthermore, including a global feature obtained from a Gaussian Mixture Model produced models with lower error than the best which could be obtained with local geographical features.en_US
dc.identifier.citationHussein, E. A. et al. (2020). Groundwater prediction using machine-learning tools. Algorithms, 13(11),300en_US
dc.identifier.issn1999-4893
dc.identifier.uri10.3390/a13110300
dc.identifier.urihttp://hdl.handle.net/10566/5769
dc.language.isoenen_US
dc.publisherMPDIen_US
dc.subjectFeature engineeringen_US
dc.subjectFull image predictionen_US
dc.subjectGlobal featuresen_US
dc.subjectTime series dataen_US
dc.subjectSquare root transformationen_US
dc.titleGroundwater prediction using machine-learning toolsen_US
dc.typeArticleen_US

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