Floods are natural disasters that often impact communities living in low-lying areas in the northern and central parts of Morocco. In this study, our aim was to create a flood susceptibility map using three methods; the hierarchy process (AHP) frequency ratio model (FR) and the weights of evidence (WoE) model. We extensively examined the area identified by these approaches using a hydraulic analysis software called HEC-RAS (version 6.3.1). Our analysis focused on the Essaouira watersheds in Morocco, where we identified around 197 flood locations. Out of these, we randomly selected 70% for modeling purposes while the remaining 30% were used for validation. Ten factors that influence floods were considered, such as slope, elevation, proximity to rivers, drainage density, stream order, land use patterns, rainfall data, lithology (permeability level) index (TWI), and curvature. We obtained these factors from data sources. Finally, we generated a flood susceptibility map and evaluated its accuracy by calculating the area under the curve (AUC). The validation results confirmed that all three models were robust and effective with an AUC of 90. Moreover, the research uncovered a trend of vulnerability with the most susceptible area being in close proximity to the city of Essaouira along the Oued Ksob. A detailed analysis using HEC-RAS was conducted at this identified location, pinpointing the village of Diabat as highly exposed. These findings hold significance for flood management, empowering decision makers, scholars, and urban planners to make informed choices and implement strategies that can minimize the impact of floods in susceptible regions while minimizing potential damages.

Identifying individuals from a facial image is a technique that forms part of computer vision and is used in various fields such as security, digital biometrics, smartphones, and banking. However, it can prove difficult due to the complexity of facial structure and the presence of variations that can affect the results. To overcome this difficulty, in this paper, we propose a combined approach that aims to improve the accuracy and robustness of facial recognition in the presence of variations. To this end, two datasets (ORL and UMIST) are used to train our model. We then began with the image pre-processing phase, which consists in applying a histogram equalization operation to adjust the gray levels over the entire image surface to improve quality and enhance the detection of features in each image. Next, the least important features are eliminated from the images using the Principal Component Analysis (PCA) method. Finally, the pre-processed images are subjected to a neural network architecture (CNN) consisting of multiple convolution layers and fully connected layers. Our simulation results show a high performance of our approach, with accuracy rates of up to 99.50% for the ORL dataset and 100% for the UMIST dataset.

Lightweight eco-materials are in high demand in many sectors, such as aerospace, industry, and building due to their several characteristics. The present paper is an experimental investigation of the thermal characteristics of novel sandwich panels made with local and ecological materials namely agglomerated cork for the core and bio-composite materials for the skin. Three configurations (symmetric, asymmetric, and two layers) were studied with different cork core thicknesses. Density values have been measured and compared. Thermal characterization consists of determining thermal conductivity and specific heat using a HFM apparatus; whilst thermal diffusivity and thermal effusivity have been calculated using the experimental findings. The panels are lightweight and thermally insulating. The values of thermal conductivity are in the range 0.071 and 0.102 W.m−1.K−1. The comparison between experimental results of thermal conductivity to theoretical values highlights the accuracy of method for multi-layer thermal characterization and the good adhesion between layers. Finally, a life cycle assessment of the new sandwich panels has been carried out and compared with common insulation materials. The sandwich panels are efficient in terms of embodied energy and CO2 emissions compared to commercialized insulators and some insulators based on recycled or natural materials, the embodied energy for symmetric configuration with 4 cm cork core are 79.73, 94.75, and 89.35 MJ/FU corresponding to an embodied carbon 5.33, 6.32, and 6.01 CO2/FU respectively. They can be classified in the middle between synthetic and natural insulators. Based on the findings, it was concluded that utilizing these sandwich panels as construction materials for interior paneling or partition walls could offer benefits in terms of being environmentally sustainable and cost-efficient.

Mountainous regions are vital for recharging aquifers in plain areas downstream, but understanding the geological, hydrogeological, and climatic factors is crucial to comprehend groundwater processes in these regions. Several parameters, including lithology, topography, secondary porosity, geological structures, and climatic conditions, affect the potential of groundwater in mountainous aquifers. Traditional groundwater modeling tools face several challenges in handling large amounts of real-time data, such as extracting useful features, quantifying uncertainty, and identifying links between different variables. Recent technological advances in artificial intelligence, particularly machine learning, provide solutions for hydrogeological research and applications. This paper focuses on modeling potential zones of groundwater sources using various methodologies based on GIS, spatial remote sensing, and machine learning. The study evaluated three models, Random Forest, Support Vector Machine, and Logistic Regression, in identifying potential groundwater zones in the Rherhaya watershed. More than 200 localized spring points were needed to ensure efficient model learning. The Support Vector Machine model demonstrated the highest performance during the 70/30% split, with a ROC-AUC of 84.4% for the test data. The study identified four critical conditioning factors of groundwater potentiality, including Topographic position index, River Distance, Valley Depth, and Plane Curvature. The models also highlighted the distance to rivers as a significant factor, particularly in the upstream portion of the watershed. The very low potentiality class occupied the largest area (over 32%), followed by low (between 24 and 29%), moderate (12–19%), high (10–14%), and very high (only 9–12%) classes. Only the Support Vector Machine model predicted that 12% of the catchment area had a high potential for groundwater resources, indicating its superior performance in identifying high-potential zones. The results offer valuable insights that can aid decision-makers in effectively managing water resources in vulnerable areas.

Abstract Disconnections due to voltage drops in the grid cannot be permitted if wind turbines (WTs) contribute significantly to electricity production, as this increases the risk of production loss and destabilizes the grid. To mitigate the negative effects of these occurrences, WTs must be able to ride through the low-voltage conditions and inject reactive current to provide dynamic voltage support. This paper investigates the low-voltage ride-through (LVRT) capability enhancement of a Type-3 WT utilizing a dynamic voltage restorer (DVR). During the grid voltage drop, the DVR quickly injects a compensating voltage to keep the stator voltage constant. This paper proposes an active disturbance rejection control (ADRC) scheme to control the rotor-side, grid-side and DVR-side converters in a wind–DVR integrated network. The performance of the Type-3 WT with DVR topology is evaluated under various test conditions using MATLAB®/Simulink®. These simulation results are also compared with the experimental results for the LVRT capability performed on a WT emulator equipped with a crowbar and direct current (DC) chopper. The simulation results demonstrate a favourable transient and steady-state response of the Type-3 wind turbine quantities defined by the LVRT codes, as well as improved reactive power support under balanced fault conditions. Under the most severe voltage drop of 95%, the stator currents, rotor currents and DC bus voltage are 1.25 pu, 1.40 pu and 1.09 UDC, respectively, conforming to the values of the LVRT codes. DVR controlled by the ADRC technique significantly increases the LVRT capabilities of a Type-3 doubly-fed induction generator-based WT under symmetrical voltage dip events. Although setting up ADRC controllers might be challenging, the proposed method has been shown to be extremely effective in reducing all kinds of internal and external disturbances.

In summary, this study addresses the critical challenge of enhancing the energy efficiency of CAEVs by navigating the complex terrain of vehicle dynamics and traffic conditions. Through a novel ECO approach, we not only harmonize these factors but also provide a tangible path toward optimal efficiency. Real-world experimentation serves as the litmus test, affirming the effectiveness of our approach and positioning it as a valuable contribution to the evolving landscape of electric vehicle technology.. Here are some comments and suggestions for improvement: