Étude de la vulnérabilité des nappes à la pollution en zones semi-arides : cas de la nappe phréatique des Béni Amir au Maroc.

Cette étude consiste à déterminer les degrés de vulnérabilité intrinsèque de la nappe des Beni Amir (Maroc) à toute forme de contaminant introduit à partir de la surface du sol. Une des méthodes les plus utilisées de caractérisation de la vulnérabilité des nappes à la pollution a été appliquée, la méthode DRASTIC modifiée (SINAN et al., 2000) couplée à un système d’information géographique. La méthode est basée sur différents paramètres : profondeur de la nappe, recharge de la nappe, nature du sol, topographie et impact de la zone non saturée. Elle permet d’élaborer une carte qui montre trois classes de vulnérabilité ; 54 % du territoire présente une vulnérabilité très faible, 38 % est caractérisé par une vulnérabilité faible et 8 % est moyennement vulnérable. Cette approche permet de contribuer à une gestion plus durable des ressources naturelles, en gérant les risques liés à la ressource en eau, en surveillant sa qualité et en actualisant les données sur la ressource.The aim of this study is to determine the level of vulnerability of Beni Amir aquifer (Morocco) to contaminants. We apply the modified DRASTIC method coupled with a Geographic Information System to characterize the vulnerability of aquifers to pollution. The modified DRASTIC method (SINAN et al., 2000) is a method based on different parameters: depth of water, net recharge, nature of the soil, topography and the impact of the vadose zone. The level of vulnerability is categorized into three classes. It was deduced that 54% of the study area has a very low vulnerability, 38% has a low vulnerability and 8% has a moderate vulnerability. This approach is relevant to contribute to the sustainable management of natural resources, managing risks related to water resources, monitoring its quality and updating data about the resource

Application of Remote Sensing Data in Lithological Discrimination of Kerdous Inlier in the Anti Atlas Belt of Morocco

Remote sensing data reveals a great importance for lithological mapping due to their spatial, spectral and radiometric characteristics. Lithological mapping using spatial data is a preliminary and important step to mineral mapping. In this work, several spectral and radiometric transformations methods were applied on Landsat 8 OLI data to enhance lithological units in the study area situated in the Anti Atlas belt. The methods of Optimum Index Factor (OIF), Decorrelation Stretching (DS), Principal Components Analysis (PCA) and Band Ratioing (BR) showed good results for lithological mapping in comparison with the existing geological and field investigation. An RGB color composite of OLI bands 651 was developed for mapping lithological units of the study area by fusing optimum index factor (OIF) and decorrelation stretching methods. furthermore, Band ratios derived from image spectra were applied in two RGB color composites (7+4/2, PC1, PC2)  and (PC1, 7/6, 3/7) providing good discrimination of the lithological units. The Landsat-8 OLI data significantly provided satisfied results for lithological mapping

Classification of a quickbird satellite image by Machine learning techniques: Mapping an urban Environement by decision tree method

Classification is a crucial stage in the processing of satellite images that influence considerably the quality of the result. A variety of methods is proposed in the literature for the purposes of image classification. They present many differences in their basic principles, thus in the quality of the results obtained. Therefore, a study of different classification methods seems to be essential. The classification of satellite images with conventional methods can be done in several ways using different algorithms. These algorithms can be divided into two main categories: supervised and non-supervised. Decision tree on the contrary is a machine learning tool. It is a plain model characterized by the simplicity of understanding and interpretation. This work aims firstly, to classify a high resolution Quickbird satellite image of an urban area by the decision tree method and compare it with the conventional classification algorithms in order to evaluate its efficiency. The methodology consists of two main stages: classification and evaluation of results. The second is based on the calculation of a number of statistical indices derived from the confusion matrix: the statistical parameter “kappa’ and the overall coefficient of precision

Mapping copper mineralization using EO-1 Hyperion data fusion with Landsat 8 OLI and Sentinel-2A in Moroccan Anti-Atlas

In Morocco, the oriental Anti-Atlas encompasses many valuable deposits, including that of copper (Cu) which is located in the Sidi Flah-Bouskour inlier. This deposit is known for its high importance on the national scale. The present work aims to compare the Hyperion raw and fused data for mapping Cu mineralization. After applying the pre-processing steps on the images used, the fusion (hyperspectral/multispectral) process was done using the colour normalized spectral sharpening method. Subsequently, the mineralogical mapping task was performed using the mixture tuned matched filtering and the independent component analysis methods. In the last step, a geophysical data, a field survey and spectroradiometric measurements were done for checking and validating the results obtained. The analysis showed a high performance of the fused images for mineralogical mapping. Consequently, the methodology proposed can be exploited in mineralogical mapping, and also in other remote sensing works

Landslide Susceptibility Mapping Using Multi-Criteria Decision-Making (MCDM), Statistical, and Machine Learning Models in the Aube Department, France

Landslides are among the most relevant and potentially damaging natural risks, causing material and human losses. The department of Aube in France is well known for several major landslide occurrences. This study focuses on the assessment of Landslide Susceptibility (LS) using the Frequency Ratio (FR) as a statistical method, the Analytic Hierarchy Process (AHP) as a Multi-Criteria Decision-Making (MCDM) method, and Random Forest (RF) and k-Nearest Neighbor (kNN) as machine learning methods in the Aube department, northeast of France. Subsequently, the thematic layers of eight landslide causative factors, including distance to hydrography, density of quarries, elevation, slope, lithology, distance to roads, distance to faults, and rainfall, were generated in the geographic information system (GIS) environment. The thematic layers were integrated and processed to map landslide susceptibility in the study area. On the other hand, an inventory of landslides was carried out based on the database created by the French Geological Survey (BRGM), where 157 landslide occurrences were selected, and then RF and kNN models were trained to generate landslide maps (LSMs) of the study area. The generated maps were assessed by using the Area Under the Receiver Operating Characteristic Curve (ROC AUC). Subsequently, the accuracy assessment of the FR model revealed more accurate results (AUC = 66.0%) than AHP, outperforming the latter by 6%, while machine learning models results showed that RF gave better results than kNN (<7.3%) with AUC = 95%. Following the analysis of LS mapping results, lithology, distance to the hydrographic network, distance to roads, and elevation were the four main factors controlling landslide susceptibility in the study area. Future mitigation and protection activities within the Aube department can benefit from the present study mapping results, implicating an optimized land management for decision-makers

Evaluation of the Impact of Gap Filling Technology in Precipitation Series on the Estimation of Climate Trends, the Case of the Souss Massa Watershed

Accurate climatic data, especially precipitation measurements, play a critical role in various studies concerning the water cycle, particularly in modeling flood and drought risks. Unfortunately, these datasets often suffer from temporary gaps that are randomly dispersed over time. This study aims to assess the effectiveness of three imputation methods: KNN, MICE, and missForest, in impute missing values in climate series. The evaluation is conducted in two distinct rainfall regimes: the Moulouya basin and the Sous Massa basin. The performance analysis considers the percentage of missing data across the entire dataset. The imputed datasets are used to estimate annual precipitation, which are then subjected to statistical tests to identify potential trends and detect changepoints. The analysis focuses on the precipitation series within the Souss Massa watershed, encompassing 27 rainfall stations. Results indicate that data imputation has a highly positive impact on the study of rainfall series trends and change point detection. The study found that studying trends without data imputation could lead to questionable conclusions. The most significant breakpoints detected in the analyzed rainfall series were in the years 1988, 1991, 1997, 2007, and 2010. The decrease in precipitation at stations showing a downward trend varies between -60 mm and -137 mm using the MICE method, and between -40 mm and 186 mm using the MissForest method