Being a component of the Eastern Boundary Upwelling (EBU) ecosystem, Morocco’s Atlantic coast presents high biological production throughout the year, with seasonal variations in upwelling dynamics. This characterization reflects the inherent nature of EBU’s ecosystems. In this work, we develop a novel methodology to compute a new upwelling index based on the analysis of sea surface temperature (SST) images. Our new upwelling index is not only simple to calculate but also efficient. Indeed, it is limited only to the upwelling region, which has allowed the improvement of the quantification and analysis of the seasonal and interannual variability of the upwelling dynamics. The new proposed upwelling index is based on the application of a recent segmentation method that allows for the monitoring of upwelling dynamics using satellite observations. The proposed upwelling index is applied to a 40-year database of weekly SST images covering the period from 1982 to 2021, and the results are used to analyze seasonal and interannual variations of the upwelling in the region.

The inshore water along the Atlantic coast of Morocco is part of one of the four major upwelling systems in the world. This region is characterized by the presence of upwelling activity almost all years around. In this work, we propose an unsupervised algorithm for identifying and extracting automatically this vital zone using the satellite observation of chlorophyll-a concentration (CHL-a). In fact, the approach is started with the application of Expectation-Maximisation algorithm for detecting the nutrient-rich region over CHL-a image. Then, a region-growing algorithm is applied to remove the noisy structures appeared in the offshore waters not belonging to the upwelling region. The proposed methodology is tested over a database of 230 weekly CHL-a images spanning the period from the years 2016 to 2020. The results show that the proposed method seems to be applicable for any other ecosystem.

Current research aims to gain an in-depth understanding of mobility characteristics in order to accurately assess each mobility defect and maintain network performance. Therefore, this article investigates the use of the Density-Based Spatial Clustering of Applications with Noise(DBSCAN) algorithm to examine the performance of a cluster within a mobility model. The six mobility models: Manhattan Grid Mobility Model(MGMM), Reference Point Group Mobility(RPGM), Nomadic Community Mobility Model (NCMM), PURSUE, Self-Similar Least Action Walk Mobility Model (SLAW), and SMOOTH, are used to evaluate the effectiveness of clustering in capturing the spatial structure of mobile models, including their spatio-temporal locations. The algorithm measures various factors, such as the number of clusters, points in each cluster, and cluster indices. We continue the study that validates synthetic mobility models, focusing on a metric known as ”Mobile Neighbors Range”. This metric allows a detailed analysis of the interactions between moving neighbors over time. In this article, a new index called ”Global Index” is introduced, based on the variation of clustering, to evaluate the movement of nodes in synthetic mobility models over time using artificial intelligence techniques, namely DBSCAN clustering, which integrates the concept of clusters. The goal is to analyze the dispersion and clustering of nodes over time in each mobility model to gain a more complete understanding of their behavior. This in-depth investigation, marked by 1,200,000 iterations, highlights the scale and precision of our efforts to delineate mobility models, thereby significantly enriching the scientific discourse on mobile networks and systems. The need for this exploration wasapparent, filling a gap in current scientific understanding