Browsing by Author "Jabrane, Meryem"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
- Differentiation of multi-parametric groups of groundwater bodies through discriminant analysis and machine LearningPublication . Mohsine, Ismail; Kacimi, Ilias; Valles, Vincent; Leblanc, Marc; El Mahrad, Badr; Dassonville, Fabrice; Kassou, Nadia; Bouramtane, Tarik; Abraham, Shiny; Touiouine, Abdessamad; Jabrane, Meryem; Touzani, Meryem; Barry, Abdoul Azize; Yameogo, Suzanne; Barbiero, LaurentIn order to facilitate the monitoring of groundwater quality in France, the groundwater bodies (GWB) in the Provence-Alpes-Cote d'Azur region have been grouped into 11 homogeneous clusters on the basis of their physico-chemical and bacteriological characteristics. This study aims to test the legitimacy of this grouping by predicting whether water samples belong to a given sampling point, GWB or group of GWBs. To this end, 8673 observations and 18 parameters were extracted from the Size-Eaux database, and this dataset was processed using discriminant analysis and various machine learning algorithms. The results indicate an accuracy of 67% using linear discriminant analysis and 69 to 83% using ML algorithms, while quadratic discriminant analysis underperforms in comparison, yielding a less accurate prediction of 59%. The importance of each parameter in the prediction was assessed using an approach combining recursive feature elimination (RFE) techniques and random forest feature importance (RFFI). Major ions show high spatial range and play the main role in discrimination, while trace elements and bacteriological parameters of high local and/or temporal variability only play a minor role. The disparity of the results according to the characteristics of the GWB groups (geography, altitude, lithology, etc.) is discussed. Validating the grouping of GWBs will enable monitoring and surveillance strategies to be redirected on the basis of fewer, homogeneous hydrogeological units, in order to optimize sustainable management of the resource by the health agencies.
- Exploring multiscale variability in groundwater quality: a comparative analysis of spatial and temporal patterns via clusteringPublication . Mohsine, Ismail; Kacimi, Ilias; Abraham, Shiny; Valles, Vincent; Barbiero, Laurent; Dassonville, Fabrice; Bahaj, Tarik; Kassou, Nadia; Touiouine, Abdessamad; Jabrane, Meryem; Touzani, Meryem; El Mahrad, Badr; Bouramtane, TarikDefining homogeneous units to optimize the monitoring and management of groundwater is a key challenge for organizations responsible for the protection of water for human consumption. However, the number of groundwater bodies (GWBs) is too large for targeted monitoring and recommendations. This study, carried out in the Provence-Alpes-Cote d'Azur region of France, is based on the intersection of two databases, one grouping together the physicochemical and bacteriological analyses of water and the other delimiting the boundaries of groundwater bodies. The extracted dataset contains 8627 measurements from 1143 observation points distributed over 63 GWB. Data conditioning through logarithmic transformation, dimensional reduction through principal component analysis, and hierarchical classification allows the grouping of GWBs into 11 homogeneous clusters. The fractions of unexplained variance (FUV) and ANOVA R-2 were calculated to assess the performance of the method at each scale. For example, for the total dissolved load (TDS) parameter, the temporal variance was quantified at 0.36 and the clustering causes a loss of information with an R-2 going from 0.63 to 0.4 from the scale of the sampling point to that of the GWB cluster. The results show that the logarithmic transformation reduces the effect of outliers and improves the quality of the GWB clustering. The groups of GWBs are homogeneous and clearly distinguishable from each other. The results can be used to define specific management and protection strategies for each group. The study also highlights the need to take into account the temporal variability of groundwater quality when implementing monitoring and management programs.