Osman, Ahmedbahaaaldin Ibrahem Ahmed and Ahmed, Ali Najah and Huang, Yuk Feng and Kumar, Pavitra and Birima, Ahmed H. and Sherif, Mohsen and Sefelnasr, Ahmed and Ebraheemand, Abdel Azim and El-Shafie, Ahmed (2022) Past, present and perspective methodology for groundwater modeling-based machine learning approaches. Archives Of Computational Methods In Engineering, 29 (6). pp. 3843-3859. ISSN 1134-3060, DOI https://doi.org/10.1007/s11831-022-09715-w.
Full text not available from this repository.Abstract
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.
| Item Type: | Article |
|---|---|
| Funders: | Innovation & Research Management Center (iRMC) of Universiti Tenaga Nasional [RJO10517844/088] |
| Uncontrolled Keywords: | Artificial neural-network; Level prediction; Ann |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 29 Aug 2023 04:31 |
| Last Modified: | 29 Aug 2023 04:31 |
| URI: | http://eprints.um.edu.my/id/eprint/40997 |
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