Statistical approach of factors controlling drainage network patterns in arid areas. Application to the Eastern Anti Atlas (Morocco)

International audience12 13 Abstract: 14 Several studies have revealed that the complexity in the distribution of drainage network patterns is not 15 random and controlled by major parameters, variable in space but also throughout geological time. Drainage 16 networks in the Eastern Anti-Atlas of Morocco consist of complex spatial arrangements with various types of 17 patterns, such as trellis, angular, dendritic and parallel. The objective was to distinguish, quantify and rank 18 the relationship that may exist between the different drainage networks patterns, geology and 19 geomorphology. A total of 230 basins were extracted from the ASTER-GDEM Elevation Data (USGS), which 20 were assigned 16 parameters reflecting their topography, morphometry, slope and geology. The statistical 21 treatment of the dataset (16 variables x 230 observations) was carried out through principal component 22 analysis (PCA), linear discriminant analysis (LDA) and agglomerative hierarchical clustering (AHC), in order to 23 investigate the complexity of drainage network patterns and their distribution. The PCA showed that the 24 topographical, slope and geological parameters, i.e. primarily the parameter associated with structural 25 control, best explains the variation in the type of the drainage pattern. The LDA made it possible to distinguish 26 between the four types of drainage patterns with a success rate of 90%, using 3 discriminant functions that 27 were better correlated with geological and slope parameters. LDA and AHC statistical treatments show 28 confusion between the parallel, trellis and angular patterns, on the one hand, due to similar factors 29 responsible for their formation, and on the other because of transitions phenomenon from one drainage 30 pattern to another over time or space. Such possible drainage network shifting may be explained by the 31 geological events that have occurred in the Eastern Anti Atlas from Lower Mesozoic to the Quaternary. 32 3

Mapping the pollution plume using the self-potential geophysical method: case of Oum Azza Landfill, Rabat, Morocco

The main landfill in the city of Rabat (Morocco) is based on sandy material containing the shallow Mio-Pliocene aquifer. The presence of a pollution plume is likely, but its extent is not known. Measurements of spontaneous potential (SP) from the soil surface were cross-referenced with direct measurements of the water table and leachates (pH, redox potential, electrical conductivity) according to the available accesses, as well as with an analysis of the landscape and the water table flows. With a few precautions during data acquisition on this resistive terrain, the results made it possible to separate the electrokinetic (~30%) and electrochemical (~70%) components responsible for the range of potentials observed (70 mV). The plume is detected in the hydrogeological downstream of the discharge, but is captured by the natural drainage network and does not extend further under the hills.info:eu-repo/semantics/publishedVersio

The contribution of remote sensing and input feature selection for groundwater level prediction using LSTM neural networks in the Oum Er-Rbia Basin, Morocco

The planning and management of groundwater in the absence of in situ climate data is a delicate task, particularly in arid regions where this resource is crucial for drinking water supplies and irrigation. Here the motivation is to evaluate the role of remote sensing data and Input feature selection method in the Long Short Term Memory (LSTM) neural network for predicting groundwater levels of five wells located in different hydrogeological contexts across the Oum Er-Rbia Basin (OER) in Morocco: irrigated plain, floodplain and low plateau area. As input descriptive variable, four remote sensing variables were used: the Integrated Multi-satellite Retrievals (IMERGE) Global Precipitation Measurement (GPM) precipitation, Moderate resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI), MODIS land surface temperature (LST), and MODIS evapotranspiration. Three LSTM models were developed, rigorously analyzed and compared. The LSTM-XGB-GS model, was optimized using the GridsearchCV method, and uses a single remote sensing variable identified by the input feature selection method XGBoost. Another optimized LSTM model was also constructed, but uses the four remote sensing variables as input (LSTM-GS). Additionally, a standalone LSTM model was established and also incorporating the four variables as inputs. Scatter plots, violin plots, Taylor diagram and three evaluation indices were used to verify the performance of the three models. The overall result showed that the LSTM-XGB-GS model was the most successful, consistently outperforming both the LSTM-GS model and the standalone LSTM model. Its remarkable accuracy is reflected in high R2 values (0.95 to 0.99 during training, 0.72 to 0.99 during testing) and the lowest RMSE values (0.03 to 0.68 m during training, 0.02 to 0.58 m during testing) and MAE values (0.02 to 0.66 m during training, 0.02 to 0.58 m during testing). The LSTM-XGB-GS model reveals how hydrodynamics, climate, and land-use influence groundwater predictions, emphasizing correlations like irrigated land-temperature link and floodplain-NDVI-evapotranspiration interaction for improved predictions. Finally, this study demonstrates the great support that remote sensing data can provide for groundwater prediction using ANN models in conditions where in situ data are lacking

Design and synthesis of triphenylphosphonium-porphyrin@xylan nanoparticles for anticancer photodynamic therapy

Most photosensitizers (PS) suffer from a lack of water solubility and from a low selectivity toward tumor cells. Delivery systems using nanoparticles make it possible to improve PS water solubility, and also tumor targeting via the enhanced permeability and retention (EPR) effect. Among the organelles, mitochondria are attractive target sites for drug-delivery strategies since they perform a variety of key cellular processes. Our study was aimed at synthesizing nanoparticles consisting of xylan-carrying porphyrins attached to a triphenylphosphonium moiety, in order to enhance the PDT effect through mitochondrial targeting. Hybrid nanoparticles were designed that consisted of a silica core coated with xylan substituted with porphyrin derivatives carrying a triphenylphosphonium moiety. These hybrid nanoparticles have been constructed, along with their counterparts devoid of silica core, taking into consideration the controversy surrounding the use of silica nanoparticles. Phototoxicity experiments, conducted against the HCT-116 and HT-29 colorectal cancer cell lines, showed that nanoparticles with porphyrins bearing a triphenylphosphonium moiety exhibited an enhanced photocytotoxic effect in comparison with free porphyrin or nanoparticles with porphyrins without the triphenylphosphonium moiety

Design and synthesis of triphenylphosphonium-porphyrin@xylan nanoparticles for anticancer photodynamic therapy

Most photosensitizers (PS) suffer from a lack of water solubility and from a low selectivity toward tumor cells. Delivery systems using nanoparticles make it possible to improve PS water solubility, and also tumor targeting via the enhanced permeability and retention (EPR) effect. Among the organelles, mitochondria are attractive target sites for drug-delivery strategies since they perform a variety of key cellular processes. Our study was aimed at synthesizing nanoparticles consisting of xylan-carrying porphyrins attached to a triphenylphosphonium moiety, in order to enhance the PDT effect through mitochondrial targeting. Hybrid nanoparticles were designed that consisted of a silica core coated with xylan substituted with porphyrin derivatives carrying a triphenylphosphonium moiety. These hybrid nanoparticles have been constructed, along with their counterparts devoid of silica core, taking into consideration the controversy surrounding the use of silica nanoparticles. Phototoxicity experiments, conducted against the HCT-116 and HT-29 colorectal cancer cell lines, showed that nanoparticles with porphyrins bearing a triphenylphosphonium moiety exhibited an enhanced photocytotoxic effect in comparison with free porphyrin or nanoparticles with porphyrins without the triphenylphosphonium moiety

Cellular vectorization and mitochondrial addressing of photosensitizers by nanoparticles formed from hardwood xylan : new hemicellulose enhancement pathway for PDT application

Les photosensibilisateurs les plus utilisés dans la Photothérapie dynamique (PDT) sont des porphyrines et leurs dérivés. Cependant, ces composés souffrent souvent d'une faible solubilité dans les milieux physiologiques et d'un manque de sélectivité envers les cellules cancéreuses, ce qui limite leurs utilisations cliniques. L’utilisation des nanoparticules comme vecteurs de photosensibilisateurs (PS) constitue une des stratégies les plus prometteuses pour surmonter ces problèmes. Dans ce contexte, nous avons développé des nanoparticules à base de xylane pour une délivrance ciblée de porphyrines. Deux types de nanoparticules ont été étudiées : des nanoparticules hybrides, noyau-coquille, et des nanoparticules organiques. Dans une première étude, les xylanesporphyrines ont été utilisés pour enrober des nanoparticules de silice (SNPs). En effet, la présence de groupes acides glucuroniques sur le xylane permet la formation de liaisons ioniques à la surface des SNPs rendues cationiques par des sels d’ammonium. L’évaluation biologique de ces nanoobjets a montré que l’association des porphyrines avec les nanoparticules augmente leur efficacité thérapeutique. De plus, l’adressage mitochondrial de ces photosensibilisateurs avec le cation triphénylphosphonium améliore encore de plus l’efficacité thérapeutique. Dans une deuxième approche, nous avons montré que les xylanes-porphyrines pouvaient conduire, par un autoassemblage, à des nanoparticules 100% organiques. Différents nano-objets à degré de substitution variable en porphyrine ont été obtenus et caractérisés. En parallèle, des nanoparticules 100% naturelles formées de xylane extrait de bois de châtaignier, et d’une chlorine naturelle, le phéophorbide a ont été obtenus et caractérisées. L’évaluation biologique de ces nanoparticules est en cours de réalisation et les premiers résultats sont très encourageants.The most used photosensitizers in photodynamic therapy (PDT) are porphyrins and their derivatives. However, these compounds often suffer from low solubility in physiological media and a lack of selectivity towards cancer cells which limits their clinical uses. One of the most promising strategies to overcome these problems is the use of nanoparticles as a vector of photosensitizers (PS). In this context, we have developed xylan-based nanoparticles for targeted delivery of porphyrins. Two types of nanoparticles have been studied: core-shell hybrid nanoparticles, and organic nanoparticles. In a first study, xylan-porphyrins were used to coat silica nanoparticles (SNPs). Indeed, the presence of glucuronic acid groups on xylan allows the formation of ionic bonds on the surface of the SNPs made cationic by ammonium salts. The biological evaluation of these nano-objects has shown that the combination of porphyrins with nanoparticles increases their photodynamic activity. In addition, mitochondrial targeting with triphenylphosphonium (TPP) increases significantly therapeutic efficacy of this PS. In a second approach, we have demonstrated that xylans-porphyrins can form nanoparticles fully organic by self-assembly in. Different nanoobjects with variable degree of substitution in porphyrins have been obtained and characterized. In parallel, 100% natural nanoparticles from chestnut xylan and natural chlorine, pheophorbide a were obtained and characterized. The biological evaluation of these nanoparticles is in progress and preliminary results are very encouragin

Vectorisation cellulaire et adressage mitochondrial de photosensibilisateurs par des nanoparticules formées de xylane de bois de feuillus : nouvelle voie de valorisation d'hémicellulose pour une application en PDT

The most used photosensitizers in photodynamic therapy (PDT) are porphyrins and their derivatives. However, these compounds often suffer from low solubility in physiological media and a lack of selectivity towards cancer cells which limits their clinical uses. One of the most promising strategies to overcome these problems is the use of nanoparticles as a vector of photosensitizers (PS). In this context, we have developed xylan-based nanoparticles for targeted delivery of porphyrins. Two types of nanoparticles have been studied: core-shell hybrid nanoparticles, and organic nanoparticles. In a first study, xylan-porphyrins were used to coat silica nanoparticles (SNPs). Indeed, the presence of glucuronic acid groups on xylan allows the formation of ionic bonds on the surface of the SNPs made cationic by ammonium salts. The biological evaluation of these nano-objects has shown that the combination of porphyrins with nanoparticles increases their photodynamic activity. In addition, mitochondrial targeting with triphenylphosphonium (TPP) increases significantly therapeutic efficacy of this PS. In a second approach, we have demonstrated that xylans-porphyrins can form nanoparticles fully organic by self-assembly in. Different nanoobjects with variable degree of substitution in porphyrins have been obtained and characterized. In parallel, 100% natural nanoparticles from chestnut xylan and natural chlorine, pheophorbide a were obtained and characterized. The biological evaluation of these nanoparticles is in progress and preliminary results are very encouragingLes photosensibilisateurs les plus utilisés dans la Photothérapie dynamique (PDT) sont des porphyrines et leurs dérivés. Cependant, ces composés souffrent souvent d'une faible solubilité dans les milieux physiologiques et d'un manque de sélectivité envers les cellules cancéreuses, ce qui limite leurs utilisations cliniques. L’utilisation des nanoparticules comme vecteurs de photosensibilisateurs (PS) constitue une des stratégies les plus prometteuses pour surmonter ces problèmes. Dans ce contexte, nous avons développé des nanoparticules à base de xylane pour une délivrance ciblée de porphyrines. Deux types de nanoparticules ont été étudiées : des nanoparticules hybrides, noyau-coquille, et des nanoparticules organiques. Dans une première étude, les xylanesporphyrines ont été utilisés pour enrober des nanoparticules de silice (SNPs). En effet, la présence de groupes acides glucuroniques sur le xylane permet la formation de liaisons ioniques à la surface des SNPs rendues cationiques par des sels d’ammonium. L’évaluation biologique de ces nanoobjets a montré que l’association des porphyrines avec les nanoparticules augmente leur efficacité thérapeutique. De plus, l’adressage mitochondrial de ces photosensibilisateurs avec le cation triphénylphosphonium améliore encore de plus l’efficacité thérapeutique. Dans une deuxième approche, nous avons montré que les xylanes-porphyrines pouvaient conduire, par un autoassemblage, à des nanoparticules 100% organiques. Différents nano-objets à degré de substitution variable en porphyrine ont été obtenus et caractérisés. En parallèle, des nanoparticules 100% naturelles formées de xylane extrait de bois de châtaignier, et d’une chlorine naturelle, le phéophorbide a ont été obtenus et caractérisées. L’évaluation biologique de ces nanoparticules est en cours de réalisation et les premiers résultats sont très encourageants

The contribution of remote sensing and input feature selection for groundwater level prediction using LSTM neural networks in the Oum Er-Rbia Basin, Morocco

The planning and management of groundwater in the absence of in situ climate data is a delicate task, particularly in arid regions where this resource is crucial for drinking water supplies and irrigation. Here the motivation is to evaluate the role of remote sensing data and Input feature selection method in the Long Short Term Memory (LSTM) neural network for predicting groundwater levels of five wells located in different hydrogeological contexts across the Oum Er-Rbia Basin (OER) in Morocco: irrigated plain, floodplain and low plateau area. As input descriptive variable, four remote sensing variables were used: the Integrated Multi-satellite Retrievals (IMERGE) Global Precipitation Measurement (GPM) precipitation, Moderate resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI), MODIS land surface temperature (LST), and MODIS evapotranspiration. Three LSTM models were developed, rigorously analyzed and compared. The LSTM-XGB-GS model, was optimized using the GridsearchCV method, and uses a single remote sensing variable identified by the input feature selection method XGBoost. Another optimized LSTM model was also constructed, but uses the four remote sensing variables as input (LSTM-GS). Additionally, a standalone LSTM model was established and also incorporating the four variables as inputs. Scatter plots, violin plots, Taylor diagram and three evaluation indices were used to verify the performance of the three models. The overall result showed that the LSTM-XGB-GS model was the most successful, consistently outperforming both the LSTM-GS model and the standalone LSTM model. Its remarkable accuracy is reflected in high R 2 values (0.95 to 0.99 during training, 0.72 to 0.99 during testing) and the lowest RMSE values (0.03 to 0.68 m during training, 0.02 to 0.58 m during testing) and MAE values (0.02 to 0.66 m during training, 0.02 to 0.58 m during testing). The LSTM-XGB-GS model reveals how hydrodynamics, climate, and land-use influence groundwater predictions, emphasizing correlations like irrigated land-temperature link and floodplain-NDVI-evapotranspiration interaction for improved predictions. Finally, this study demonstrates the great support that remote sensing data can provide for groundwater prediction using ANN models in conditions where in situ data are lacking

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

Spatio-Temporal Analysis of the Remote Sensing Ecological Index – A Case Study of the Favorable Agro-Ecological Zone in Northwest Morocco

Agriculture has traditionally been one of Morocco's most important industries, providing the largest percentage of the nation's GDP (Gross Domestic Product). However, over the past two decades, the frequency and severity of Morocco's droughts have grown. These climate changes have a direct impact on essential crops in the country. Exploring the geographical and temporal evolution of the ecological quality is thus critical for the conservation of the natural environment. To achieve this, the present study attempted to evaluate seasonally the environmental quality in the most favorable agro-ecologic zone in Morocco, using remote sensing data, in the years 2001, 2011, and 2021. An index was created, called Remote Sensing Environmental Index (RSEI), which combines four ecological indicators, related to vegetation, humidity, heat, and dryness aspects. The results indicate that from 2011 to 2021, the RSEI values deteriorated the greatest, particularly during the winter months. In addition, vegetation and humidity were the parameters most affecting the RSEI index. Thus, the key drivers of the improvement in the environmental quality are the establishment of ecological policies, rules, and actions to maintain a sustainable environmental development