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Abdella Mohamed

Publications and source records attributed to Abdella Mohamed.

2 recordsLinked to original sources

Trans-Arctic route feasibility on a pan-Arctic grid under bathymetric and sea-ice constraints

Climate driven reductions in Arctic sea ice have renewed interest in trans Arctic shipping, but adoption remains limited by basic questions of route feasibility, safety and excess distance. Existing studies mostly compare idealised great circle shortcuts or use full weather routing systems, leaving a gap for simple basin scale diagnostics on realistic bathymetry and sea ice. We develop an offline graph based framework on a 0.5 degree pan Arctic grid that combines GEBCO 2024 bathymetry with a summer 2018 Arctic sea ice reanalysis from the Copernicus Marine Environment Monitoring Service (CMEMS). An A* pathfinding algorithm is applied to a canonical Europe Asia origin destination pair to quantify route availability and route length inflation relative to a great circle. Enforcing sea only feasibility increases route length by about 10 percent before depth and ice constraints are applied. Depth thresholds representative of under keel clearance (hmin = 20-50 m) remove up to roughly 15 percent of the sea mask but preserve a trans Arctic connection for hmin = 20 m. Summer sea ice exerts a strong seasonal control: continuous ice safe routes emerge only from mid August, with distances inflated by roughly 20-25 percent even in late summer. When depth and ice constraints are imposed jointly, only about 75 percent of sea cells remain safe and no continuous joint safe trans Arctic route exists in the tested season. The framework provides a basin scale screening tool for Arctic shipping and a baseline for forecast driven, multi objective routing studies.

physics.geo-ph

Data Fusion and Machine Learning for Ship Fuel Consumption Modelling -- A Case of Bulk Carrier Vessel

There is an increasing push for operational measures to reduce ships' bunker fuel consumption and carbon emissions, driven by the International Maritime Organization (IMO) mandates. Key performance indicators such as the Energy Efficiency Operational Indicator (EEOI) focus on fuel efficiency. Strategies like trim optimization, virtual arrival, and green routing have emerged. The theoretical basis for these approaches lies in accurate prediction of fuel consumption as a function of sailing speed, displacement, trim, climate, and sea state. This study utilized 296 voyage reports from a bulk carrier vessel over one year (November 16, 2021 to November 21, 2022) and 28 parameters, integrating hydrometeorological big data from the Copernicus Marine Environment Monitoring Service (CMEMS) with 19 parameters and the European Centre for Medium-Range Weather Forecasts (ECMWF) with 61 parameters. The objective was to evaluate whether fusing external public data sources enhances modeling accuracy and to highlight the most influential parameters affecting fuel consumption. The results reveal a strong potential for machine learning techniques to predict ship fuel consumption accurately by combining voyage reports with climate and sea data. However, validation on similar classes of vessels remains necessary to confirm generalizability.

cs.LG