Past, Present and Perspective Methodology for Groundwater Modeling-Based Machine Learning Approaches
Machine learning; Numerical methods; Water levels; Ground water level; Groundwater modelling; Hydrological variables; Level model; Machine learning approaches; Meteorological variables; Model method; Model-based OPC; Rapid urbanizations; Water level variations; Groundwater
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Springer Science and Business Media B.V.
2023
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my.uniten.dspace-267422023-05-29T17:36:28Z Past, Present and Perspective Methodology for Groundwater Modeling-Based Machine Learning Approaches Osman A.I.A. Ahmed A.N. Huang Y.F. Kumar P. Birima A.H. Sherif M. Sefelnasr A. Ebraheemand A.A. El-Shafie A. 57437554300 57214837520 55807263900 57206939156 23466519000 7005414714 6505592467 57437700400 16068189400 Machine learning; Numerical methods; Water levels; Ground water level; Groundwater modelling; Hydrological variables; Level model; Machine learning approaches; Meteorological variables; Model method; Model-based OPC; Rapid urbanizations; Water level variations; Groundwater Growing population and rapid urbanization are among the major causes of ground water level (GWL) depletion. Modeling GWL is considered as tough task as the GWL variation depends on various complex hydrological and meteorological variables. However, few methodologies have been proposed in literature for modeling GWL. The present research offers a summary of the most common methodologies in GWL forecasting using artificial intelligence (AI), as well as bibliographic assessments of the authors' knowledge and an overview and comparison of the findings. The characteristics and capabilities of modeling methods and the consideration of input data types and time steps have been reviewed in 40 studies published from 2010 to 2020. The reviewed studies succeeded in modeling and predicting the GWL in various regions using the methods proposed by the authors. Trial and error method in certain phases of AI modeling was helpful for testing in special applications for GWL modeling. The reviewed papers provided several partial and overall findings that may provide relevant recommendations to investigators who would like to conduct similar work in GWL modeling. In this report, a variety of new concepts for designing novel approaches and enhancing modeling efficiency are also discussed in the relevant field of analysis. Analyzing modeling methods used in all the reviewed studies it was estimated that the machine learning methods are efficient enough for modeling GWL. � 2022, The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE). Final 2023-05-29T09:36:27Z 2023-05-29T09:36:27Z 2022 Review 10.1007/s11831-022-09715-w 2-s2.0-85123946020 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123946020&doi=10.1007%2fs11831-022-09715-w&partnerID=40&md5=2b5ff576c9d7858fd99ba1336d3cae41 https://irepository.uniten.edu.my/handle/123456789/26742 29 6 3843 3859 Springer Science and Business Media B.V. Scopus |
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| description |
Machine learning; Numerical methods; Water levels; Ground water level; Groundwater modelling; Hydrological variables; Level model; Machine learning approaches; Meteorological variables; Model method; Model-based OPC; Rapid urbanizations; Water level variations; Groundwater |
| author2 |
57437554300 |
| author_facet |
57437554300 Osman A.I.A. Ahmed A.N. Huang Y.F. Kumar P. Birima A.H. Sherif M. Sefelnasr A. Ebraheemand A.A. El-Shafie A. |
| format |
Review |
| author |
Osman A.I.A. Ahmed A.N. Huang Y.F. Kumar P. Birima A.H. Sherif M. Sefelnasr A. Ebraheemand A.A. El-Shafie A. |
| spellingShingle |
Osman A.I.A. Ahmed A.N. Huang Y.F. Kumar P. Birima A.H. Sherif M. Sefelnasr A. Ebraheemand A.A. El-Shafie A. Past, Present and Perspective Methodology for Groundwater Modeling-Based Machine Learning Approaches |
| author_sort |
Osman A.I.A. |
| title |
Past, Present and Perspective Methodology for Groundwater Modeling-Based Machine Learning Approaches |
| title_short |
Past, Present and Perspective Methodology for Groundwater Modeling-Based Machine Learning Approaches |
| title_full |
Past, Present and Perspective Methodology for Groundwater Modeling-Based Machine Learning Approaches |
| title_fullStr |
Past, Present and Perspective Methodology for Groundwater Modeling-Based Machine Learning Approaches |
| title_full_unstemmed |
Past, Present and Perspective Methodology for Groundwater Modeling-Based Machine Learning Approaches |
| title_sort |
past, present and perspective methodology for groundwater modeling-based machine learning approaches |
| publisher |
Springer Science and Business Media B.V. |
| publishDate |
2023 |
| _version_ |
1806425803354800128 |
| score |
13.222552 |