Omar El Kharki

Wildfires represent a growing environmental and socio-economic threat across Mediterranean landscapes, where prolonged summer droughts and human activity increasingly shape ignition susceptibility. This study presents an open and reproducible modelling framework for comparing the relative influence of anthropogenic and biophysical drivers of wildfire ignition susceptibility across selected Mediterranean regions. Using harmonized 500 m predictors derived from global remote-sensing datasets, we integrate vegetation condition, topography, climatic context, and human pressure indicators within a cloud-based Google Earth Engine workflow. Two tree-based machine-learning models (Random Forest and Extreme Gradient Boosting) are trained and evaluated using spatial cross-validation and cross-region transfer experiments. Results consistently highlight the dominant role of anthropogenic pressure in shaping ignition susceptibility across all study areas, with night-time lights and human modification indices contributing to the largest share of model importance. Both models achieve high predictive performance (AUC > 0.90) and retain stable accuracy under cross-region transfer (mean transfer AUC ≈ 0.85), indicating partial generalization of human-driven ignition patterns across Mediterranean landscapes. Beyond predictive performance, the principal contribution of this work lies in its harmonized cross-regional comparison and explicit evaluation of model transferability using open data and scalable cloud processing. The resulting susceptibility maps provide a transparent and operational basis for comparative wildfire risk assessment and prevention planning within comparable Mediterranean contexts.

To sum up, that study investigates the impact of the generative AI adoption through different factors influencing the adoption of Generative Artificial Intelligence (AI) in the preparation of courses and the development of assessments by Moroccan university teachers. That study hereby gathers four hundred faculty members from eleven Moroccan public universities. It is a comparison between Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) using binary logistic regression. The model performance is assessed by using Akaike Information Criterion (AIC) and Area Under the Curve (AUC) metrics. It indicates that AI is much more used for course preparation, at a percentage of 59.3%. Besides, assessment creation represents 43.5%. Those statistics are different because it has to keep an intact academic integrity and AI hallucinations. The faculty uses AI to simplify complex ideas and create new ones. The statistic results indicate that the TAM is, by far, the best predictor in AI adoption. If it is compared to the UTAUT, the AIC and the AUC indicators are higher (AIC = 184.27, AUC = 0.94) than UTAUT (AIC = 480.82, AUC = 0.61). To conclude, Moroccan faculty members consider the use of AI pragmatic and individualistic. It emphasizes on the institutions need to establish professional programs, dedicated to development and transparent assessment practices, considering AI integration.

Tangier’s northern Atlantic coastal zone, particularly from Achakar to Sidi Kacem, is under increasing pressure from urbanization and tourism, which exacerbates its vulnerability to coastal erosion. This study aimed to assess spatio-temporal changes, quantify erosion and accretion rates, and identify factors influencing the coastline’s stability or retreat. Potential impacts of this erosion include the loss of coastal habitats and threats to local biodiversity. The methodology combined automatic shoreline extraction using multi-temporal Landsat images (1973–2024) and field-based DGPS surveys. The results show that the coastline is eroding at an average rate of –2.24 m year–1, with erosion rates increasing in severity towards the south. Two beaches, Sidi Kacem and Houara, were studied in detail. Between 1973 and 1990, significant erosion occurred: –6.63 m year–1 at Sidi Kacem and –8.01 m year–1 at Houara. Between 1990 and 2015, erosion (–2.12 m year–1 at Houara) and accretion (+2.24 m year–1 at Sidi Kacem) fluctuated. From 2015 to 2024, alternating dynamics with reduced intensity were observed. While this erosion poses limited immediate threats to critical infrastructure such as the airport and road networks, the study highlights limitations, including satellite image resolution and temporal data gaps. Future research should integrate higher-resolution data (e.g. LIDAR) and evaluate anthropogenic impacts on coastal dynamics, including urban runoff, sand extraction and tourism infrastructure development, to improve management strategies in the Achakar–Sidi Kacem area.

T Tangier, one of Morocco’s largest coastal cities strategically located between two maritime boundaries, has undergone significant transformations, particularly through the revitalization of its port area, leading to the establishment of a coastal path and waterfront. Given this context, conducting large-scale, precise spatiotemporal analysis and land-use classification is crucial for fostering Tangier’s sustainable development. In this paper, a random forest algorithm was utilized to map land use changes in Tangier city using Landsat images, which has led to the generation of land use maps of the city from 2000 to 2023. The results show high accuracy in identifying land-use classes with an overall accuracy of 96% and a kappa coefficient exceeding 0.96. Moreover, the spatiotemporal analysis revealed notable changes: built-up land and forested areas increased by 124.55% and 41.11%, respectively, while maritime water areas decreased by 5%, and vegetation areas experienced a 30% decrease, on the other hand. The techniques presented in this study contribute to enhancing the precision of land-use classification within the complex environment of Tangier. They provide valuable technical support for conducting natural resource surveys and promoting sustainable regional development in the city.

Forest fires have become a major concern in the northern parts of Morocco, particularly in the Tangier-Tetouan-Al Hoceima (TTA) region, causing significant damage to the environment and human lives. To address this pressing issue, this study proposes an approach that utilizes remote sensing (RS) and machine learning (ML) techniques to detect burned areas in the TTA region within the Google Earth Engine platform. The study focuses on burned areas resulting from forest fires in three specific locations in the TTA region that have experienced such fires in recent years, namely Tangier-Assilah in 2017, M’diq Fnideq in 2020, and Chefchaouen in 2021. In our study, we extensively explored multiple combinations of spectral indices, such as normalized burn ratio (dNBR), normalized difference vegetation index (dNDVI), soil-adjusted vegetation index (dSAVI), and burned area index (dBAI), in conjunction with Sentinel-2 (S2) satellite images. These combinations were employed within the Random Forest (RF) algorithm, allowing us to draw important conclusions. Initially, we assess the individual effectiveness of the dNBR index, which yields accuracy rates of 83%, 90%, and 82% for Tangier-Assilah, Chefchaouen, and M’diq Fnideq, respectively. Recognizing the need for improved outcomes, we expand our analysis by incorporating spectral indices and S2 bands. However, the results obtained from this expanded combination lack consistency and stability across different locations. While Tangier-Assilah and M’diq Fnideq experience accuracy improvements, reaching 95% and 88%, respectively, the inclusion of Sentinel bands has an adverse effect on Chefchaouen, resulting in a decreased accuracy of 87%. To achieve optimal accuracy, our focus shifted towards the combination of dNBR and the other spectral indices. The results were truly remarkable, with accuracy rates of 96%, 97%, and 97% achieved for Tangier-Assilah, Chefchaouen, and M’diq Fnideq, respectively. Our decision to prioritize the spectral indices was based on the feature importance method, which highlights the significance of each feature in the classification process. The practical implications of our study extend to fire management and prevention in the TTA region. The insights gained from our analysis can inform the development of effective policies and strategies to mitigate the impact of forest fires. By harnessing the potential of RS and ML techniques, along with the utilization of spectral indices, we pave the way for enhanced fire monitoring and response capabilities in the region.

The use of a Machine Learning (ML) classification algorithm to classify airborne urban Light Detection And Ranging (LiDAR) point clouds into main classes such as buildings, terrain, and vegetation has been widely accepted. This paper assesses two strategies to enhance the effectiveness of the Deep Learning (DL) classification algorithm. Two ML classification approaches are developed and compared in this context. These approaches utilize the DL Pipeline Network (DLPN), which is tailored to minimize classification errors and maximize accuracy. The geometric features calculated from a point and its neighborhood are analyzed to select the features that will be used in the input layer of the classification algorithm. To evaluate the contribution of the proposed approach, five point-clouds datasets with different urban typologies and ground topography are employed. These point clouds exhibit variations in point density, accuracy, and the type of aircraft used (drone and plane). This diversity in the tested point clouds enables the assessment of the algorithm’s efficiency. The obtained high classification accuracy between 89% and 98% confirms the efficacy of the developed algorithm. Finally, the results of the adopted algorithm are compared with both rule-based and ML algorithms, providing insights into the positioning of DL classification algorithms among other strategies suggested in the literature.