Apport de la géophysique à la détermination du remplissage sédimentaire et de la position des niveaux aquifères du Bassin côtier Dradere Soueire (Maroc Nord Occidental)

The Drader Souiere basin is a part of the hydrological basin of Sebou that is known by a high agricultural activity. The work aims to establish the relationship between sedimentary filling and aquifer facies distribution as well as to provide a new approach to interpretation, interpolation and identification. In this paper, a methodology that combines, a geological field and boreholes data with geophysical data (tomography, logging and seismic), is adopted and integrated, in a Geographic Information System (GIS), in order to establish isobaths map of Tortonian-Messinian marls bedrock, to determine the sequential position of Pliocene-Quaternary aquifers and to highlight the roles of deep structures in these aquifers arrangement. Tortonian-Messinian marls constitute the basin substrate; deposited on the Pre-Rif layers, from where he inherits his structuration, in the form of NW-SE ripples (an anticlinal-synclinal succession). The Pliocene-Quaternary sedimentary filling by paleo-channels, sea level oscillations and Miocene syn-sedimentary tectonics, give to the basin a «piano keys» geometry with various sedimentary environments. The most important aquifers of the basin match the regressive sea level periods of Zanclean and middle-upper Pleistocene. These new data provide other perspectives to quantitative research by hydrodynamic modeling of the water resources basin.La cuenca Dradere Soueire pertenece a la gran cuenca del río Sebou, conocida por una actividad agrícola muy importante. El objetivo del trabajo es establecer la relación entre el relleno  de sedimentos y la distribución  de facies sedimentarios de los  acuíferos y también aportar un nuevo enfoque a la interpretación, interpolación e identificación. En el presente estudio, los métodos utilizados combinan datos geológicos de terreno y de sondeos, así como datos geofísicos (tomografía, diagrafía y sísmica), integrados en un Sistema de Información Geográfica (SIG),con un fin de establecer el mapa de isobaras del sustrato de margas Torto-Mesinienses, y determinar la posición secuencial de los acuíferos del Plio-Cuaternario además de poner en relieve el papel de las estructuras profundas en la disposición de estos acuíferos. El sustrato  a nivel de la cuenca es de edad Torto-Mesiniense, depositado sobre los mantos pre-rifeños, de donde hereda su estructura, en forma de ondulaciónes NO-SE (una sucesión  pliegues anticlinal-sinclinal). Se debe destacar que el relleno sedimentario pliocuaternario por paleocanales antiguos, las oscilaciones del nivel del mar y la presencia de movimientos tectónicos  sinsedimentarios del Mioceno, confieren a la cuenca una geometría «en teclas de piano» con contextos sedimentarios que varían. Así mismo los acuíferos más importantes de la cuenca coinciden con los períodos regresivos de los niveles de Zancliense y Pleistoceno medio y superior.Estos nuevos datos abren nuevas perspectivas para estudios cuantitativos por modelización hidrodinámica.Le bassin Dradere Soueire fait partie du bassin hydrologique du Sebou qui est connu par une activité agricole très importante. Le travail vise à établir la relation entre le remplissage sédimentaire et la distribution des faciès aquifères ainsi que d’apporter une nouvelle approche d’interprétation, d’interpolation et d’identification. Dans la présente étude, une méthodologie où sont combinées, les données géologiques de terrain et de forages, ainsi que des données géophysiques (tomographie, diagraphie et sismique), est adoptée et intégrée dans un Système d’Information Géographique (SIG), afin d’établir la carte des isobathes du substratum marneux torto-messenien, de déterminer la position séquentielle des aquifères plio-quaternaires et de mettre en évidence le rôle des structures profondes dans l’agencement de ces aquifères. Le substratum au niveau du bassin est marneux d’âge torto-messenien, déposé sur des nappes pré rifaines, d’où il hérite sa structuration, sous forme d’ondulations NW-SE (une succession anticlinal-synclinal). Le remplissage sédimentaire plio-quaternaire par des anciens paléo-chenaux, les oscillations du niveau marin et la présence de mouvements tectoniques syn-sédimentaires miocènes, confèrent au bassin une géométrie « en touches de piano » avec des contextes sédimentaires variant. Les aquifères les plus importants du bassin coïncident avec les périodes régressives du niveau marin d’âge zancléen et pléistocène moyen et supérieur. Ces nouvelles données ouvrent de nouvelles perspectives aux études quantitatives par modélisation hydrodynamique

Contribution of geophysics to determine the sedimentary filling and aquifers levels position within Dradere Soueire coastal basin (North Western Morocco)

The Drader Souiere basin is a part of the hydrological basin of Sebou that is known by a high agricultural activity. The work aims to establish the relationship between sedimentary filling and aquifer facies distribution as well as to provide a new approach to interpretation, interpolation and identification. In this paper, a methodology that combines, a geological field and boreholes data with geophysical data (tomography, logging and seismic), is adopted and integrated, in a Geographic Information System (GIS), in order to establish isobaths map of Tortonian-Messinian marls bedrock, to determine the sequential position of Pliocene-Quaternary aquifers and to highlight the roles of deep structures in these aquifers arrangement. Tortonian-Messinian marls constitute the basin substrate; deposited on the Pre-Rif layers, from where he inherits his structuration, in the form of NW-SE ripples (an anticlinal-synclinal succession). The Pliocene-Quaternary sedimentary filling by paleo-channels, sea level oscillations and Miocene syn-sedimentary tectonics, give to the basin a «piano keys» geometry with various sedimentary environments. The most important aquifers of the basin match the regressive sea level periods of Zanclean and middle-upper Pleistocene. These new data provide other perspectives to quantitative research by hydrodynamic modeling of the water resources basin.La cuenca Dradere Soueire pertenece a la gran cuenca del río Sebou, conocida por una actividad agrícola muy importante. El objetivo del trabajo es establecer la relación entre el relleno de sedimentos y la distribución de facies sedimentarios de los acuíferos y también aportar un nuevo enfoque a la interpretación, interpolación e identificación. En el presente estudio, los métodos utilizados combinan datos geológicos de terreno y de sondeos, así como datos geofísicos (tomografía, diagrafía y sísmica), integrados en un Sistema de Información Geográfica (SIG),con un fin de establecer el mapa de isobaras del sustrato de margas Torto-Mesinienses, y determinar la posición secuencial de los acuíferos del Plio-Cuaternario además de poner en relieve el papel de las estructuras profundas en la disposición de estos acuíferos. El sustrato a nivel de la cuenca es de edad Torto-Mesiniense, depositado sobre los mantos pre-rifeños, de donde hereda su estructura, en forma de ondulaciónes NO-SE (una sucesión pliegues anticlinal-sinclinal). Se debe destacar que el relleno sedimentario pliocuaternario por paleocanales antiguos, las oscilaciones del nivel del mar y la presencia de movimientos tectónicos sinsedimentarios del Mioceno, confieren a la cuenca una geometría «en teclas de piano» con contextos sedimentarios que varían. Así mismo los acuíferos más importantes de la cuenca coinciden con los períodos regresivos de los niveles de Zancliense y Pleistoceno medio y superior.Estos nuevos datos abren nuevas perspectivas para estudios cuantitativos por modelización hidrodinámic

Landslide hazard assessment in the heterogeneous geomorphological and environmental context of the rif region, morocco – A machine learning approach

International audienceLandslides are considered to be one of the most significant and critical natural hazards in the heterogeneous geomorphological setting of the Rif region of Morocco. Despite the high susceptibility to landslides, the region lacks detailed studies. Therefore, this research introduces four advanced machine learning methods, namely Support Vector Machine (SVM), Classification and Regression Trees (CART), Multivariate Discriminant Analysis (MDA), and Logistic Regression (LR), to perform landslide susceptibility mapping, as well as study of the connection between landslide occurrence and the complex regional geo-environmental context of Taounate province. Fifteen causative factors were extracted, and 255 landslide events were identified through fieldwork and satellite imagery analysis. All models performed very well (AUC > 0.954), while the CART model performed the best (AUC= 0.971). However, SVM demonstrated superior performance compared to other methods, achieving the highest accuracy (89.92%) and F1-measure (81.66%) scores on the training data, and the highest accuracy (83.01%), precision (81.74%), and specificity (79.46%) scores on the test data. The results do not necessarily indicate that LR and MDA have the lowest predictive ability, as they demonstrated high accuracy in terms of AUC and in some classification tasks. Moreover, they provide the significant advantage of easy interpretation of the geo-environmental processes that control landslides. Rainfall is the primary triggering factor of landslides in the study area. The majority of landslides occurred on slopes, particularly those located along rivers and faults, suggesting that landslides in the region are closely associated with active tectonics and precipitation. All four models predicted similar spatial distribution patterns in landslide susceptibility. The results showed that almost half of the area mainly in the north and northwest, has a very high susceptibility to landslides. The findings provide valuable references for land use management and the implementation of effective measures for landslide prevention

L'impact de la décharge d'Oum Azza sur la qualité des eaux souterraines au niveau de la région de Rabat (Maroc)

International audienceA 110 ha dump, located in the Akrach shallow aquifer area in Morocco, was studied in order to characterize the quality of groundwater around the landfill, to delineate the contaminated area and to determine the factors favouring the contamination of groundwater. This dump is located in the city of Oum Azza (15 km south from Rabat), (Map 1). between the Akrach River in the west and the Sidi Mohamed Ben Abdellah Dam in the east. To assess groundwater pollution from this landfill, piezometric monitoring and Hydrochemical analyses were conducted on 22 wells. The results highlight a significant degradation in the groundwater quality, especially in the parts located in the direct vicinity downstream the dump. In the impacted areas, electrical conductivity was above 1100 μS cm-1, bicarbonates were higher than 508 mg L-1, chlorides, sulphate, and nitrates contents were higher than 850, 200 and 1 mg L-1, respectively. In addition we found high cadmium (50 - 100 μg L-1) and chromium (40 - 230 μg L-1) contents, i.e. much higher than the WHO guidelines for drinking or irrigation water. A principal component analysis conducted on this dataset highlights the absence of a prevalent principal component (PC), with the variance distributed across many PCs. The mechanisms responsible for the chemical variance of the hydro system are therefore numerous and fairly balanced in terms of influence. Finally, a hierarchical classification brings out three groups of observations, each group corresponding to a level of pollution.Un vertedero de 110 ha, ubicado sobre el acuífero superficial de Akrach en Marruecos, ha sido estudiado para caracterizar a la calidad de las aguas subterráneas al rededor del vertedero, de conocer la extensión del área contaminada y estudiar a los factores involucrados en esta contaminación. El vertedero se ubicado en la ciudad de Um Azza (15 km al sur de Rabat) (mapa 1), entre el rio Akrach al oeste y la represa Sidi Mohaed Ben Abdallah al este. Un control piezómetro y análisis químicos se hicieron sobre 22 pozos para estudiar esta contaminación. La calidad del agua es afectada por una contaminación importante especialmente en las áreas al contacto cercano del vertedero y aguas abajo de este mismo. En las zonas afectadas la conductividad eléctrica de las aguas supera 1100 µS.cm-1 , los bicarbonatos superan 508 mg.L-1 , los cloruros, sulfatos y nitratos superan 850,200 y 1 mg.L-1. Además se consiguió alto contenido en cadmio (50-100 µg.L-1) y cromo (40-230 µg.L-1) o sea mucho más que las normas de la OMS para el agua para beber o regar. Un análisis en componentes principales e llevo a cabo sobre el conjunto de datos. No aparece un eje factorial fuerte sino más bien muchos ejes de semejante fuerza, lo que muestra un conjunto de mecanismos de misma fuerza involucrados en la calidad química de las aguas contaminadas. Una clasificación jerárquica ascendente da tres grupos de muestras, cada grupo corresponde a un nivel de contaminación

The impact of the Oum Azza landfill on the quality of groundwater at the Rabat region (Morocco)

A 110 ha dump, located in the Akrach shallow aquifer area in Morocco, was studied in order to characterize the quality of groundwater around the landfill, to delineate the contaminated area and to determine the factors favouring the contamination of groundwater. This dump is located in the city of Oum Azza (15 km south from Rabat), (Map 1). between the Akrach River in the west and the Sidi Mohamed Ben Abdellah Dam in the east. To assess groundwater pollution from this landfill, piezometric monitoring and Hydrochemical analyses were conducted on 22 wells. The results highlight a significant degradation in the groundwater quality, especially in the parts located in the direct vicinity downstream the dump. In the impacted areas, electrical conductivity was above 1100 μS cm-1, bicarbonates were higher than 508 mg L-1, chlorides, sulphate, and nitrates contents were higher than 850, 200 and 1 mg L-1, respectively. In addition we found high cadmium (50 - 100 μg L-1) and chromium (40 - 230 μg L-1) contents, i.e. much higher than the WHO guidelines for drinking or irrigation water. A principal component analysis conducted on this dataset highlights the absence of a prevalent principal component (PC), with the variance distributed across many PCs. The mechanisms responsible for the chemical variance of the hydro system are therefore numerous and fairly balanced in terms of influence. Finally, a hierarchical classification brings out three groups of observations, each group corresponding to a level of pollution.The impact of the Oum Azza landfill on the quality of groundwater at the Rabat region (Morocco) L’impact de la decharge d’Oum Azza sur la qualite des eaux souterain region de Rabat (Maroc) La décharge étudiée est localisée dans la zone de l’aquifère peu profond d’Akrach (Map 1). Elle est d’une superficie de 110 ha, située sur la commune d’Oum Azza à 15 Km de Rabat, entre la rivière Akrach à l’ouest et le barrage de Sidi Mohamed Ben Abdellah à l’est. Le but de cette étude est de caractériser la qualité des eaux souterraines autour de la décharge, de délimiter la zone contaminée et de déterminer les facteurs favorisant la contamination des eaux souterraines. Pour évaluer la pollution des eaux souterraines due à cette décharge, des analyses de niveau piézométrique et Hydrochimique ont été réalisées sur 22 puits. Les résultats des analyses géochimiques montrent une dégradation qualitative importante des eaux souterraines, en particulier dans les parties situées dans la zone de gradient descendant et à proximité directe de la décharge. Dans ces zones polluées, nous avons observé les valeurs suivantes: supérieure à 1100 μS/cm en conductivité électrique, 508mg/L en bicarbonates, 850mg/L et 200mg/L respectivement en chlorures et sulfate, 1mg/L en nitrates, 50 - 100 mg/L en cadmium, et 40 - 230 μg/L en chrome. Ces concentrations dépassent largement les valeurs standards de l’Organisation Mondiale de la Santé (OMS) pour l’eau potable et l’eau d’irrigation. Une analyse en composantes principales réalisée sur cet ensemble de données met en évidence l’absence d’une composante principale (PC) prévalente, la variance étant répartie sur de nombreux PC. Les mécanismes responsables de la variance chimique d’hydro système sont donc nombreux et assez équilibrés en termes d’influence. Enfin, une classification hiérarchique fait apparaître trois groupes d’observations, chaque groupe correspondant à un niveau de pollution.Un vertedero de 110 ha, ubicado sobre el acuífero superficial de Akrach en Marruecos, ha sido estudiado para caracterizar la calidad de las aguas subterráneas alrededor del vertedero, conocer la extensión del área contaminada y estudiar los factores involucrados en esta contaminación. El vertedero se emplazó en la ciudad de Um Azza (15 km al sur de Rabat), entre el rio Akrach, al oeste, y la represa Sidi Mohaed Ben Abdallah, al este. Un control piezómetro y análisis químicos se hicieron sobre 22 pozos para estudiar esta contaminación. La calidad del agua es afectada por una contaminación importante, especialmente en las áreas al contacto cercano del vertedero, y aguas abajo de este mismo. En las zonas afectadas la conductividad eléctrica de las aguas supera 1100 µS.cm-1, los bicarbonatos superan 508 mg.L-1 , los cloruros, sulfatos y nitratos superan 850,200 y 1 mg.L-1. Además se consiguió alto contenido en cadmio (50-100 µg.L-1) y cromo (40- 230 µg.L-1). En definitiva, se superaron olgadamente los valores estipulados en las normas de la OMS para el agua potable y de riego. Un análisis en componentes principales se llevó a cabo sobre el conjunto de datos. No aparece un eje factorial consistente, sino más bien muchos ejes de importancia similar, lo que muestra un conjunto de mecanismos de la misma fuerza involucrados en la calidad química de las aguas contaminadas. Una clasificación jerárquica ascendente da tres grupos de muestras, cada grupo corresponde a un nivel de contaminación

Exploring Multiscale Variability in Groundwater Quality: A Comparative Analysis of Spatial and Temporal Patterns via Clustering

Defining 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-Côte 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 R2 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 R2 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

Drainage Network Patterns Determinism: A Comparison in Arid, Semi-Arid and Semi-Humid Area of Morocco Using Multifactorial Approach

International audienceDrainage network patterns influence the hydrological response of the watersheds and must be taken into account in the management of the water resource. In this context, it is important to identify the factors that control the configuration of drainage networks in and beyond specific climatic conditions. Here, we study 318 basins spread over three sectors (arid, semi-arid, and semi-humid) of Morocco where seven drainage network patterns have been identified. From each basin, 14 parameters were extracted, describing the relief, geology, morphometry, drainage network, land cover, precipitation, and time of concentration (Tc). Principal component analysis (PCA) and discriminant analysis (DA) processing were performed on the entire database and on each sector separately. The results show that the drainage network pattern is a feature of the landscape that contributes significantly to the variance of the basins. They suggest that the distribution of network patterns is controlled by the relationship between the different parameters, mainly those related to the relief, more than by the variations of each parameter taken individually. The network discrimination rate is 63.8%, which improves when each sector is treated separately. Confusion in discrimination are similar across all sectors and can be explained by similar conditions (active tectonic, deformation, and uplift) or transitions from one network pattern to another, due to the landscape evolution of certain sectors. A contribution of climatic variables appears locally but was attributed to a statistical coincidence, these parameters presenting a distribution close to that of the relief and geology variables

Multivariate Analysis and Machine Learning Approach for Mapping the Variability and Vulnerability of Urban Flooding: The Case of Tangier City, Morocco

International audienceUrban flooding is a complex natural hazard, driven by the interaction between several parameters related to urban development in a context of climate change, which makes it highly variable in space and time and challenging to predict. In this study, we apply a multivariate analysis method (PCA) and four machine learning algorithms to investigate and map the variability and vulnerability of urban floods in the city of Tangier, northern Morocco. Thirteen parameters that could potentially affect urban flooding were selected and divided into two categories: geo-environmental parameters and socio-economic parameters. PCA processing allowed identifying and classifying six principal components (PCs), totaling 73% of the initial information. The scores of the parameters on the PCs and the spatial distribution of the PCs allow to highlight the interconnection between the topographic properties and urban characteristics (population density and building density) as the main source of variability of flooding, followed by the relationship between the drainage (drainage density and distance to channels) and urban properties. All four machine learning algorithms show excellent performance in predicting urban flood vulnerability (ROC curve > 0.9). The Classifications and Regression Tree and Support Vector Machine models show the best prediction performance (ACC = 91.6%). Urban flood vulnerability maps highlight, on the one hand, low lands with a high drainage density and recent buildings, and on the other, higher, steep-sloping areas with old buildings and a high population density, as areas of high to very-high vulnerability

Differentiation of Multi-Parametric Groups of Groundwater Bodies through Discriminant Analysis and Machine Learning

International audienceIn order to facilitate the monitoring of groundwater quality in France, the groundwater bodies (GWB) in the Provence-Alpes-Côte 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