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Amidst the rising prevalence of electric vehicles (EV) and the corresponding need for adequate charging infrastructures, this study presents an innovative application of deep learning models to analyze urban traffic. Utilizing the YOLO v3 model, our research focuses on developing a prototype for counting vehicles at an urban roundabout connecting three main roads, providing essential data for planning EV charging infrastructures. This work involves adapting and optimizing the YOLO v3 model to accurately identify and count vehicles passing through the roundabout. The aim is to measure traffic volume and identify circulation trends, which are crucial for understanding the spatial and temporal distribution of vehicles. The collected data not only estimate the number of EVs likely to traverse this area but also predict the needs for charging infrastructure in and around this specific roundabout. Our prototype demonstrated high accuracy in vehicle counting, allowing detailed analysis of traffic patterns at different times of the day. This analysis reveals significant variations in traffic flows, providing valuable insights into the most suitable times and locations for installing EV charging stations. While this study focuses on a single roundabout, it establishes a precedent for using advanced deep learning techniques in assessing EV charging infrastructure needs in broader urban contexts. The results suggest that such methods can be extended to other urban areas for more comprehensive and effective planning of EV charging infrastructures, thus contributing to more sustainable and intelligent urban mobility management. [ABSTRACT FROM AUTHOR]

A large portion of Morocco's forest ecosystem is being damaged by human activity and climate change, which makes it crucial for the country to monitor forest dynamics and develop measures to counter these effects. The objective of this study was to determine how the forest cover has changed over the past four decades in Bouskoura forest, Morocco. Based on Remote Sensing (RS), Geographic Information System (GIS) and machine learning algorithms. Throughout the process, Spatial data such as Landsat 5 TM, Sentinel-2B, and spectral indices, including NDVI, NDWI, NDBI, and MSAVI2 were used to train/validate Random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN) classifiers. By comparing the performance of the three classifiers for all four periods, the RF method was the most effective with an overall accuracy of 0.99 and kappa coefficient of 0.99 for 1991, 2001 and 2011, and an overall accuracy of 0.99 and kappa coefficient of 0.98 for 2021. Therefore, the RF was selected as a method of examining time variations. The results indicated that forests covered an area of 20,41 km2 in 1991 which has decreased to 18,96 km2 in 2021, a loss of 1,45 km2 (7.10%) in four decades. The highest forest loss was 2,69 km2 during 1991–2001, 2,12 km2 during 2001–2011, 1,40 km2 during 2011–2021. And the highest forest gain was found to be 3,75 km2 during 2011–2021, 0,61 km2 during 2001–2011, 0,43 km2 during 1991-2001. Recent declines in forest degradation attest to the benefits of initiatives to conserve the environment taken by the country. [ABSTRACT FROM AUTHOR]

The Oualidia lagoon on the Atlantic coast of Morocco provides important ecosystem services such as aquaculture, fisheries, tourism and high ecological and biological productivity. This is the first study to describe the spatio-temporal distribution of the crustacean community and potential controlling factors in the coastal waters of the Oualidia lagoon. Crustaceans were sampled with a Van Veen grab during two surveys in winter and summer 2013, and taxonomic composition and diversity were determined at 43 sampling sites. Of the eighteen crustacean taxa recorded, fifteen were new to Oualidia Lagoon. Sphaeroma serratum was the most abundant species in both seasons. Organic matter and chlorophyll a content were higher, temperature and salinity were lower in winter than in summer. The structure of the crustacean assemblages was characterised by the formation of two main clusters, organised according to a downstream gradient. Canonical Correspondence Analysis (CCA) showed that granulometry, organic matter and salinity strongly influenced the distribution pattern of crustaceans in the lagoon. [ABSTRACT FROM AUTHOR]

Agriculture is a very important economic sector in Morocco, which requires a set of tools to improve agricultural production. Among these tools, the use of geomembrane basins. The latter is of great importance in smart farming planning, management practices or even in livestock use. In this context, this study evaluates a recognition and classification of the geomembrane basin using remote sensing satellite images; based on the Yolov3 deep learning neural network. This paper first adjusts the network model to make it suitable for detecting small targets on remote sensing images, then uses the k-means algorithm to calculate the grid size of the Yolo network model suitable for geomembrane basins, then uses yolov3 to train the data that makes up the satellite remote sensing imagery. The network model for the detection of the geomembrane basins is obtained by the test phase. Finally, the geomembrane basin detection model adapted to the remote sensing image is obtained in the validation phase. Through the research and analysis of the experimental results, it can be seen that this method effectively detects the geomembrane basins in the remote sensing images and ensures the high detection accuracy of the experimental results, which gave us an accuracy of 75%. [ABSTRACT FROM AUTHOR]

Currently there is a public awareness to protect the environment especially forest ecosystems and the forest fire dilemma has become a topic of intense research around the world. In this setting, this study evaluates forest fire susceptibility (FFS) in northern Morocco using three geographic information system (GIS) based on machine learning algorithms: XGBoost (XGB), Random Forest (RF), and Support Vector Machine (SVM). To this effect, a Geographic Information System (GIS) database was developed involving ten independent causal factors (elevation, aspect, slope, distance to roads, distance to residential areas, land cover, normalized difference vegetation index (NDVI), rainfall, temperature and wind speed) and 345 fire pixels. The 345 pixels were split into two sets for training (70%) and validation (30%) and the spatial relationships between factors affecting FFs and fire pixels was analyzed using XGB, RF, and SVM models to generate the FFS maps. The effectiveness of the models was evaluated using the receiver operating characteristic curve, the area under the curve (AUC), and several statistical measures. The results of the three models hinted that XGBoost had the highest performance (AUC = 0.856), followed by RF (AUC) = 0.827), and SVM (AUC = 0.803) in the forecasting of the forest fire. The resulting FFS maps areas can provide crucial support for the management of Mediterranean forest ecosystems and can enhance the effectiveness of planning and management of forest resources and ecological balances in these areas. [ABSTRACT FROM AUTHOR]

Globally, Increasing greenhouse production is causing producing environmental issues. Monitoring the number and determining the locations of greenhouses are essential for the modern agricultural land management. The aim of this study was to detect plastic greenhouses using remote sensing data and the YOLOv5 CNN-based object detection algorithm. In the process, 426 images of plastic greenhouses were manually downloaded from Google and Mohammed 6 satellites and were divided into three datasets, training (63%), validation (15%), and testing (22%). Plastic greenhouse images in the training and validation sets were manually labeled, with the training set being used to train the YOLO-V5 Model. Throughout the training, the validation dataset was used to evaluate how well the network performed. According to the performance results the system reached its highest level of accuracy at epoch 71, with a relatively brief training time of 40 minutes, The model's average precision was 84.1 percent, Using satellite images. In detecting plastic greenhouses, YOLOv5 can deliver exceptional accuracy and computational performance. [ABSTRACT FROM AUTHOR]

Accurate and efficient extraction of rural settlements from high-resolution remote sensing imagery is of paramount importance for rural government management. Unplanned rural settlements are quite common. Understanding the spatial characteristic of these rural settlements is of great importance as it offers indispensable information for land management and decision-making. In this setting, the U-net architecture is proposed in this study for rural settlements differentiation by image segmentation on high-resolution satellite images of rural settlements in Zagora province, Draa-Tafilalet region, Morocco. To predict pixels in remote sensing images representing rural settlements in this province. Image segmentation is conducted using different encoders in the U-net architecture, and the results are compared. Experimental results demonstrate that the proposed method effectively mapped and discriminated rural settlements areas with an overall accuracy of 98%, achieving comparable and improved performance over other traditional rural extraction methods. [ABSTRACT FROM AUTHOR]

PbTiO3 (PT) sol gel processed powders doped with lanthanum (PLT14) and cerium (PLCexT14, x = 1; 3; 6; 10) were characterized by XRD and Raman spectroscopy. The samples crystallize in a pure perovskite phase at temperatures smaller than those reported for the same compounds prepared by other conventional methods. The effect of cerium or/and lanthanum on the crystal structure, microstructural and dielectric properties of PbTiO3 were investigated using FTIR, XRD, SEM, Raman spectroscopy and impedance spectroscopy. Incorporation of Ce reduces the temperature of crystallization of the samples, the temperature of the maximum of the permittivity and the diffuseness character of the ferroelectric to paraelectric transition, and enhances the permittivity.

By 2050, Most of the world's population will live in cities, this demographic explosion will lead to significant urban development at the expanse of natural land which may harm the environmental quality. Consequently, assessing and modeling the urban environmental quality (UEQ) is requisite for efficient urban sprawl control and better city planning and management. The present study proposes a methodology to model and assess the environment of the urban system by developing the urban environmental quality index (UEQI) based on remote sensing data. Five environmental indicators were derived from the Landsat OLI image namely, Modified Normalized Difference Impervious Surface Index (MNDISI), Modified Normalized Difference, Water Index (MNDWI), Normalized difference vegetation Index (NDVI), Normalized difference built-up Index (NDBI) and Soil adjusted vegetation index (SAVI). Using the Principal Component Analysis (PCA) the urban environmental quality index was computed for the 17 communes of Casablanca city. The UEQI values were spatially mapped under three classes (good, moderate, and poor). The results obtained from the analysis showed a significant difference in the term of UEQI values among the communes. In addition, the environmental quality is inadequate in communes with fewer green spaces and more impervious surfaces. The outcomes of this work can serve as an efficient tool to determine the most critical interventions to be made by the authority for current and future urban planning and land/resource management. [ABSTRACT FROM AUTHOR]

Coastal lagoons are highly dynamic and physically complicated systems. They are environmentally productive and socio-economically valuable. Contemporary global development and management pressures require a better understanding of their dynamics and sustainability. The present study focuses on the problem of water confinement in the Oualidia lagoon (Atlantic coast of Morocco). This lagoon is characterized by an asymmetric tidal propagation, with a shorter duration of the flood (rising tide) than the ebb (falling tide). In the long term, this contributes to the reduction of depths and the confinement of water upstream. After extensive studies, a sediment trap was created in 2011 to trap the finest sediment in the upstream part of the lagoon. This study aims to analyze the morphodynamical and sedimentological changes in the lagoon of Oualidia, after the sediment trap dredging. For this purpose, bathymetric surveys covering 6 years between 2006 and 2012 were analyzed, providing sufficient data to identify the morphological changes that the lagoon has undergone during this period. The data analysis was followed by a study of the lagoon bed dynamics using profile lines extracted from the bathymetric data in a GIS environment. As a result, the findings partly show that over 6 years, an average height of +0.65 m was gained by the lagoon, while the average change in the eroded areas was estimated to be −0.42 m. In addition, the eroded area in the lagoon was estimated to be about 1,513,800 m2 with an erosion volume of 633,383 m3, while the accumulated area found was about 2,699,396 m2 with an accumulation volume of 1,765,866 m3. These changes can be related to the large input of marine sediment, mainly caused by tidal currents and waves, but also to the creation of a sediment trap in the upstream area of the lagoon. [ABSTRACT FROM AUTHOR]

During the last years, Morocco's Environmental, Social and Touristic perspectives has improved significantly, but it remains one of the biggest challenges for Mohammedia city. The purpose of this study is to locate an optimal site for the construction of a new Waste Water Treatment Plant (WWTP) in order to help the environment, to select an appropriate site for the construction of a new School that will benefit the social needs of the city and to create a suitable tour plan of touristic sites for tourism. It was carried out by using Geographic Information System (GIS), Remote Sensing (RS), Global Positioning System (GPS) devices, Digital cameras and Multi- criteria Decision Analysis (MCDA).Throughout the process, the used data for both WWTP and School selection include the remote sensing data of multi spectral satellite imageries and the digital elevation model combined with vector data of land use (LU) layers, and for Tour creation the used data include collected photographs and GPS data from the site. According to MCDA, the results presents an optimal site with an area of 48283,02 m2 for a new WWTP, a suitable area with an area of 3200 m² for a new School and a city tour for tourists with a distance of 27 km. [ABSTRACT FROM AUTHOR]

Since the industrial revolution, the world is experiencing a huge change in its climate, which causes many imbalances such as flash floods (FF). The aim of this study is to propose a new approach for detection and forecasting of flash flood susceptibility in the city of Tetouan, Morocco. For this regard, support vector machine (SVM), logistic regression (LR), random forest (RF), Naïve Bayes (NB) and Artificial neural network (ANN) are used based on 1101 points (680 flood points and 421 non-flood points) and 9 flash-flood predictors (Elevation , Slope , Aspect , LU/LC , Stream Power Index , Plan curvature , Profile Curvature , Topographic Position Index and Topographic Wetness Index) that were extracted from the DEM (10m resolution) and satellite imagery (Sentinel 2B) of the study area. Models were trained on 70% and tested on 30% of this dataset also they were evaluated using several metrics such as the Receiver Operating Characteristic (ROC) Curve, precision, recall, score and kappa index. The result demonstrated that RF (AUC = 0.99, Accuracy = 96%, Kappa statistics = 0.92) has the highest performance, followed by ANN (AUC = 0.98, Accuracy = 95%, Kappa statistics = 0.89) and SVM (AUC = 0.96, Accuracy = 92%, Kappa statistics = 0.80). The proposed approach is an effective tool for forecasting and predicting FF that can help reduce the severity of this disaster. [ABSTRACT FROM AUTHOR]

Accelerated urban growth has affected many of the planet's natural processes. In cities, most of the surface is covered with asphalt and cement, which has changed the water and air cycles. To restore the balance of urban ecosystems, cities must find the means to create green spaces in an increasingly gray world. Green spaces provide the city and its inhabitants a better living environment. This article uses Nador city as a case study area, this project consists in studying the possibility for the roofs to receive vegetation. The first axis of this project is the quantification of the current vegetation cover at ground level by calculating the Normalized Difference Vegetation Index (NDVI) based on Satellite images Landsat 8, then the classification of the LiDAR point cloud, and the generation of a digital surface model (DSM) of the urban area. This type of derived data was used as the basis for the various stages of estimating the potential plant cover at the roof level. In order to study the different possible scenarios, a set of criteria was applied, such as the minimum roof area, the inclination and the duration of the sunshine on the roof, which is calculated using the linear model of angstrom Prescott based on solar radiation. The study shows that in the most conservative scenario, 21771 suitable buildings that had to be redeveloped into green roofs, with an appropriate surface area of 369.26Ha allowing a 63,40% increase in the city's green space by compared to the current state contributing to the improvement of the quality of life and urban comfort. The average budget for the installation of green roofs in a building with a surface area of 100 m2 varies between 60000dh and 170000dh depending on the type of green roofs used, extensive or intensive. These results would enable planners and researchers in green architecture sciences to carry out more detailed planning analyzes. [ABSTRACT FROM AUTHOR]

In the last 2 decades, Morocco has known rapid growth of urban transformation followed by significant Population growth, which causes serious environmental problems related to water pollution and scarcity, and social with the deficiencies of infrastructures. And this has been witnessed in the city of Biougra which requires taking serious steps and adopting new projects to solve these issues as soon as possible. And as a reflection of that, this paper takes advantage of Geographic information system (GIS) coupled tools in the first place to locate future sites for building new schools using weighted overlay analysis approach, to improve the education system. And in the second place to choose potential sites for implementing new wastewater treatment plant (WWTP) by adopting multicriteria analysis (MCA). Finally, as a part of saving cultural heritage, and improving the social and economic situation for local citizens, we aim to value cooperative of this region as a replacement of poor touristic quality in the city, by making a track of the existing cooperative and profit from 3d modeling as a part of providing the traveler the best possible guide to reach these points of interest and also develop a desktop application for editing and manipulating different types of file related to cooperatives. This study showed successful results by localizing a new site to build a school in the northeastern of the city, and a match between the found site for WWTP and the existing station but with giving a possibility for expansion. [ABSTRACT FROM AUTHOR]

Cheating in exams is a worldwide phenomenon that hinders efforts to assess the skills and growth of students. With scientific and technological progress, it has become possible to develop detection systems in particular a system to monitor the movements and gestures of the candidates during the exam. Individually or collectively. Deep learning (DL) concepts are widely used to investigate image processing and machine learning applications. Our system is based on the advances in artificial intelligence, particularly 3D Convolutional Neural Network (3D CNN), object detector methods, OpenCV and especially Google Tensor Flow, to provides a real-time optimized Computer Vision. The proposal approach, we provide a detection system able to predict fraud during exams. Using the 3D CNN to generate a model from 7,638 selected images and objects detector to identify prohibited things. These experimental studies provide a detection performance with 95% accuracy of correlation between the training and validation data set. [ABSTRACT FROM AUTHOR]

Storms represent an increased source of risk that affects human life, property, and the environment. Prediction of these events, however, is challenging due to their low frequency of occurrence. This paper proposed an artificial intelligence approach to address this challenge and predict storm characteristics and occurrence using a gated recurrent unit (GRU) neural network and a support vector machine (SVM). Historical weather and marine measurements collected from buoy data, as well as a database of storms containing all the extreme events that occurred in Brittany and Pays de la Loire regions, Western France, since 1996, were used. Firstly, GRU was used to predict the characteristics of storms (wind speed, pressure, humidity, temperature, and wave height). Then, SVM was introduced to identify storm-specific patterns and predict storm occurrence. The approach adopted leads to the prediction of storms and their characteristics, which could be used widely to reduce the awful consequences of these natural disasters by taking preventive measures. [ABSTRACT FROM AUTHOR]

Terrestrial ecosystems store more carbon (C) than the atmosphere and provide ecosystem services (ES) such as global climate regulation, by sequestering carbon within biomass and soil. Land use land cover (LULC) change is considered a key factor, playing an important role in the dynamic variations of carbon storage. The aim of this paper is to assess the effects that LULC has had on carbon stocks and consequently on climate change regulation in north-western Morocco over 21 years. To achieve this aim, the Integrated Valuation of ES and Trade-offs (InVEST) model is used to assess status and variation in the net amount of carbon stored by the different types of LULC, and the economic value of the carbon sequestered in the remaining stock. The results show that the total carbon stock increased from 4.81TgC in 1996 to 4.98TgC in 2017. Over the 21 years, the LULC changes had the greatest effect on carbon storage - an increase of 6.87% with 0.17TgC of carbon sequestered, since the majority of unused land was changed to forest and cultivated land. Based on the global costs of atmospheric carbon, we estimate the economic value of carbon storage services to be between US$1,800,000 and US$3,570,000 for the whole period, with an average yearly increment of between US$86,000 and US$170,000. The results show that the ecosystem management has had a substantial climate mitigation effect. Also, the possibility of paying for ES could inform policy on the adoption of LULC to support livelihood and management choices. [ABSTRACT FROM AUTHOR]