Differentiation of multi-parametric groups of groundwater bodies through discriminant analysis and machine Learning

• Autor(a) principal: Mohsine, Ismail
• Data de Publicação: 2023
• Outros Autores: 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, Laurent
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.1/20311
• topic: Groundwater bodies; Machine learning; Discriminant analysis; Chemical composition; Bacteriological composition; PACA region; France

In 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.