Assessing the Impact of Port Structure on Shoreline Evolution: Case Study of Tangier Med Port, Morocco

Coastal development can significantly impact nearby shorelines, raising concerns about erosion and sustainability. This study investigates the long-term effects of the Tangier Med port on the coastlines of Dalia and Ksar Sghir beaches in Morocco using remote sensing techniques and the Digital Shoreline Analysis System (DSAS). A multi-decadal approach analyzed shoreline changes for pre-construction (1988-2005) and post-construction (2004-2022) periods. Our findings reveal contrasting patterns. Ksar Sghir displayed accretion, due to port infrastructure disrupting natural sediment transport. Dalia beach exhibited a mixed response with ongoing erosion and localized accretion, influenced by the construction of a fishing port on the eastern side of the beach. To gain further insights into the future trajectory of these shorelines, we employed a Kalman filter model to predict shoreline positions for the year 2034 and 2044. These predictions highlight the potential for continued divergence between the beaches. Dalia beach faces potential infrastructure damage on the western side due to ongoing erosion, while the eastern side may require increased dredging to maintain fishing port access. Ksar Sghir beach, on the other hand, is projected to experience continued accretion with minimal anticipated negative impacts. This research offers valuable insights into the differential impacts of port development on adjacent coastlines. It highlights the importance of long-term monitoring and pre-construction data for understanding coastal interventions. Additionally, incorporating shoreline forecasting can aid in developing sustainable management strategies for both coastlines and port functionality

K nearest neighbors classification of water masses in the western Alboran Sea using the sigma-pi diagram

Different classification techniques of water masses have been developped using the potential temperature-salinity (θ-S) diagram and its volumetric analysis. In this study, we propose a new method to automatically classify water masses via a supervised machine learning algorithm based on the K nearest neighbors (Knn), in the potential density and potential spicity (σ-π) coordinates. This method is applied to temperature and salinity data collected in the western side of the Alboran Sea during a glider mission, dedicated to sample the Western Alboran Gyre (WAG) in late winter 2021. The water masses in the studied region were classified into five different categories following a supervised learning process, based on ocean profile databases available on the region of interest. The results corroborate previous studies of the spatial distribution of water masses in the Alboran Sea, inferred from traditional method based on the expert analysis of the (θ-S) diagram, and suggest that this methodology is efficient and reliable for water masses classification. Compared to a classical clustering computation (herein k-means), this method is more appropriate in a region where the characteristics of the water masses change considerably in both space and time

A diachronic study of the Mediterranean coastline: A geometric approach

Coastal areas represent one of the country's most important environmental and economic resources. They are naturally dynamic, with changes that can occur on a human time scale and that need to be quantified for the proper management of shorelines and, in particular, the beaches on which the local economy is largely based. This work focuses on the diachronic study of the coastline of the Mediterranean coast, particularly the coastal fringe at the mouth of the Wadi Aliane. In order to assess and remedy the risks of erosion and accretion of the coastline, the methodology followed consists of the application of automatic analytical techniques using multi-temporal photo-interpretation, a Geographic Information System (GIS) and a Digital Shoreline Analysis System (DSAS). The rate of change will be calculated from the multi-date maps, (1981- 1997 and 2016) using the End Point Rate (EPR) index. Comparison of the results of the interpretation of aerial photos and satellite images of the Oued Aliane coastline used (1981, 1997, and 2016) provided information that allowed us to understand the evolutionary behaviour of the wet sand/dry sand line over 36 years. This numerical analysis of the 1981 -1997 and 2016 coastlines in the coastal sector of Oued Aliane, shows us that zones A, C and D are mainly affected by erosion, while the mouth part is affected by accretion because it is considered a delta and therefore a sedimentation area

K nearest neighbors classification of water masses in the western Alboran Sea using the sigma-pi diagram

Different classification techniques of water masses have been developped using the potential temperature-salinity (θ-S) diagram and its volumetric analysis. In this study, we propose a new method to automatically classify water masses via a supervised machine learning algorithm based on the K nearest neighbors (Knn), in the potential density and potential spicity (σ-π) coordinates. This method is applied to temperature and salinity data collected in the western side of the Alboran Sea during a glider mission, dedicated to sample the Western Alboran Gyre (WAG) in late winter 2021. The water masses in the studied region were classified into five different categories following a supervised learning process, based on ocean profile databases available on the region of interest. The results corroborate previous studies of the spatial distribution of water masses in the Alboran Sea, inferred from traditional method based on the expert analysis of the (θ-S) diagram, and suggest that this methodology is efficient and reliable for water masses classification. Compared to a classical clustering computation (herein k-means), this method is more appropriate in a region where the characteristics of the water masses change considerably in both space and time