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| DOI | 10.1007/s13201-024-02162-x |
| Contribution to advancing aquifer geometric mapping using machine learning and deep learning techniques: a case study of the AL Haouz-Mejjate aquifer, Marrakech, Morocco | |
| 发表日期 | 2024 |
| ISSN | 2190-5487 |
| EISSN | 2190-5495 |
| 起始页码 | 14 |
| 结束页码 | 5 |
| 卷号 | 14期号:5 |
| 英文摘要 | Groundwater resources in Morocco often face sustainability challenges due to increased exploitation and climate change. Specifically, the Al-Haouz-Mejjate groundwater in the Marrakesh region is faced with overexploitation and insufficient recharge. However, the complex subsurface geometries hamper hydrogeological modeling, characterization, and effective management. Reliably estimating aquifer substrate topography is critical for groundwater models but is challenged by limited direct measurements. This study develops nonlinear machine learning models to infer substrate depths by fusing sparse borehole logs with regional geospatial data. A Gaussian process regression approach provided robust holistic mapping, leveraging flexibility, and uncertainty quantification. Supplementary neural network architectures focus on isolating specific variable relationships, like surface elevation-substrate. Model accuracy exceeded 0.8 R-squared against validation boreholes. Spatial visualizations confirmed consistency across landscape transects. Elevation and piezometric data proved most predictive, though multivariate inputs were required for the lowest errors. The results highlight the power of statistical learning to extract meaningful patterns from disparate hydrological data. However, model opacity and the need for broader training datasets remain barriers. Overall, the work demonstrates advanced machine learning as a promising avenue for illuminating complex aquifer geometries essential for sustainability. Hybrid approaches that use both data-driven and physics-based methods can help solve long-standing problems with hydrogeological characterization. |
| 英文关键词 | Aquifer geometry prediction; Aquifer substrate assessment; Geospatial parameters; Supervised machine learning |
| 语种 | 英语 |
| WOS研究方向 | Water Resources |
| WOS类目 | Water Resources |
| WOS记录号 | WOS:001202054800001 |
| 来源期刊 | APPLIED WATER SCIENCE
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/296842 |
| 作者单位 | Mohammed VI Polytechnic University; Cadi Ayyad University of Marrakech; Mohammed VI Polytechnic University; Ibn Zohr University of Agadir |
| 推荐引用方式 GB/T 7714 | . Contribution to advancing aquifer geometric mapping using machine learning and deep learning techniques: a case study of the AL Haouz-Mejjate aquifer, Marrakech, Morocco[J],2024,14(5). |
| APA | (2024).Contribution to advancing aquifer geometric mapping using machine learning and deep learning techniques: a case study of the AL Haouz-Mejjate aquifer, Marrakech, Morocco.APPLIED WATER SCIENCE,14(5). |
| MLA | "Contribution to advancing aquifer geometric mapping using machine learning and deep learning techniques: a case study of the AL Haouz-Mejjate aquifer, Marrakech, Morocco".APPLIED WATER SCIENCE 14.5(2024). |
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