Prediction and control of nitrate concentrations in groundwater by implementing a model based on GIS and artificial neural networks (ANN)
| Autor: | Fadi Chaaban, Eric Masson, Hanan Darwishe, Barbara Louche, Erick Carlier, Jamal El Khattabi |
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| Přispěvatelé: | Laboratoire Génie Civil et Géo-Environnement [Béthune] (LGCgE), Université d'Artois (UA), Centre Régional d'Innovation et de Transferts Technologiques des industries du bois (CRITT Bois), CRITT Bois, Laboratoire de Mécanique de Lille - FRE 3723 (LML), Université de Lille, Sciences et Technologies-Centrale Lille-Centre National de la Recherche Scientifique (CNRS), Université de Lille, Sciences et Technologies-Ecole Centrale de Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0208 environmental biotechnology
Soil Science Aquifer 02 engineering and technology 010501 environmental sciences 01 natural sciences [SPI]Engineering Sciences [physics] Environmental Chemistry ComputingMilieux_MISCELLANEOUS 0105 earth and related environmental sciences Earth-Surface Processes Water Science and Technology Hydrology Global and Planetary Change geography geography.geographical_feature_category Hydrogeology MODFLOW Probabilistic logic Geology Groundwater recharge Pollution 6. Clean water 020801 environmental engineering 13. Climate action [SDE]Environmental Sciences Environmental science Water quality Groundwater model Groundwater |
| Zdroj: | Environmental Earth Sciences Environmental Earth Sciences, Springer, 2017, 76 (19), ⟨10.1007/s12665-017-6990-1⟩ |
| ISSN: | 1866-6280 1866-6299 |
| DOI: | 10.1007/s12665-017-6990-1⟩ |
| Popis: | Groundwater modelling has become a major step for decision support in integrated water resource management, but groundwater models require accurate and spatially distributed data to provide reliable results. Hydrogeological modelling of these data can be implemented with physically based models (i.e. MODFLOW, MT3D…). Other approaches that are simpler to implement may be a good substitute for these numerical approaches. This is the case of probabilistic approaches and especially the statistical approach neural networks. The proposed method (coupling GIS/ANN) is especially suitable for the problem of large-scale and long-term simulation. It has been applied in the spatial prediction of nitrates in the chalk aquifer in Bethune (North of France). This confined chalk aquifer in its northern part provides natural denitrification and ensures a good drinking water quality, while in its southern part this aquifer is facing a high level of nitrate concentrations far above the European Nitrates Directive standard. A good groundwater management of this ecosystems service is therefore of great importance for regional water management. Thus, the spatial distribution of nitrate concentration obtained by GIS/ANN coupling model was compared with the results obtained from the numerical modelling (MT3D) and validated by the real measurements. ANN modelling seems to be more realistic than MT3D modelling both for 2003 and 2004. This is true for both of the nitrate concentrations and their difference. So, ANN modelling’s spatially distributed difference with observed data ranges from − 3.67 to + 1.24 mg/l in 2003 and − 10.8 to + 6.51 mg/l in 2004, whereas for the MT3D model, this difference ranges from − 11.5 to + 17.9 mg/l in 2003 and − 9.91 to + 16.9 mg/l in 2004. The satisfactory results of the ANN model allowed to launch prospective simulations for 2025 under two groundwater recharge scenarios: a deficit year (150 mm/year) and a rainy year (500 mm/year) show an expansion of the exploitable zone ([NO3–] |
| Databáze: | OpenAIRE |
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