This research introduces an innovative method employing the Canny edge detector for automatic and precise coastline extraction, aiming to analyze spatial and temporal variations in the Oman coastline from 2000 to 2022 using GIS and remote sensing (RS) techniques. Focusing on both multi-decadal and short-term periods, the study aims to detect accretion and erosion rates through the observation and interpretation of coastal changes. Utilizing the Digital Shoreline Analysis System and LANDSAT imageries, Shoreline changes have been quantitatively evaluated using three distinct approaches: Linear Regression Rate (LRR), End Point Rate (EPR), and Net Shoreline Movement (NSM). The dynamic nature of the Oman coastal region necessitates a comprehensive understanding of its evolving coastline. Our investigation applies digital shoreline analysis to discern shifts in the coastline’s position, employing a multiple regression approach for quantifying the rate of coastal change. To facilitate automatic shoreline extraction, various methods were experimented with, ultimately determining the Canny Edge algorithm’s superiority in yielding precise results. The paper outlines the monitoring procedures for the coastal area and analyzes coastline changes using geospatial techniques. This analysis provides valuable insights for the planning and management of the Oman shore. Furthermore, the proposed model’s applicability is rigorously tested against other generic edge detection algorithms, including Sobel, Prewitt, and Robert’s techniques. The conclusive findings demonstrate that our model outperforms these alternatives, particularly excelling in the accurate detection of the coastline. This research contributes to a deeper understanding of coastal dynamics and offers a robust methodology for coastal monitoring, with implications for effective planning and management strategies in the Oman shore region.

International Maritime Organization (IMO) has established mandatory measures for improving the energy efficiency of ships in accordance with its reduction targets for greenhouse gas (GHG) emissions. As a result of the Paris Agreement, the maritime industry has been under tremendous pressure to reduce emissions of greenhouse gases since its inception. By 2050, the international shipping industry must make progress toward reducing greenhouse gas emissions by at least 50% from 2008 levels, according to resolutions adopted by the IMO. As part of the UN Sustainable Development Goal 13 (SDG 13), the IMO calls on governments to take immediate action to combat climate change. In order to achieve this goal, efficiency improvements must be combined with the use of alternative low-carbon fuels, as well as an increase in technical energy efficiency through the use of more efficient ship design and energy efficiency technologies (EETs). For the purpose of understanding maritime decarbonization strategy during the transition period and providing energy efficiency and elevating energy efficiency on board, legal aspects, framework, and requirements must be clearly understood for today and the future. There is a transition of the regulations for GHG emission reduction by the early implementation of the Energy Efficiency Design Index (EEDI) Phase 3 requirements for LNG carriers, general cargo ships, container ships, large LPG carriers, and cruise passenger ships. In the original schedule, it was decided to come into force in 2025. With the aim to solve the shipping industry decarbonization earlier,

This literature review critically examines the development and optimization of sustainable energy and exergy analysis software specifically designed for offshore wind farms, emphasizing the transformative role of machine learning (ML) in overcoming operational challenges. Offshore wind energy represents a cornerstone in the global transition to low-carbon economies due to its scalability and superior energy yields; however, its complex operational environment, characterized by harsh marine conditions and logistical constraints, necessitates innovative analytical tools. Traditional deterministic methods often fail to capture the dynamic interactions within wind farms, thereby underscoring the need for ML-integrated approaches that enhance precision in energy forecasting, fault detection, and exergy analysis. This PRISMA-ScR review synthesizes recent advancements in ML techniques, including Random Forest, Long Short-Term Memory networks, and hybrid models, demonstrating significant improvements in predictive accuracy and operational efficiency. In addition, it critically identifies current gaps in existing software tools, such as inadequate real-time data processing and limited user interface design, which hinder the practical implementation of ML solutions. By integrating theoretical insights with empirical evidence, this study proposes a unified framework that leverages ML algorithms to optimize turbine performance, reduce maintenance costs, and minimize environmental impacts. Emerging trends, such as incorporating digital twins and Internet of Things (IoT) technologies, further enhance the potential for real-time system monitoring and adaptive control. Overall, this review provides a comprehensive roadmap for the next generation of software tools to revolutionize offshore wind farm management, thereby aligning technological innovation with global renewable energy targets and sustainable development goals.

Maritime spatial planning (MSP) is a mechanism of supporting adaptive decision making in response to possible conflicts over offshore wind turbine locations. The main purpose of this study is to determine the location of potential offshore wind turbine sites in the Northern Aegean Sea in Turkey by creating a geographical information systems-based marine spatial planning system for the region. A decision model has been proposed in which GIS and multi-criteria decision-making techniques are used to determine alternative areas. While the analytic hierarchy process (AHP) was utilized to identify viable sites based on several essential physical factors in this study, the Fuzzy TOPSIS approach was used to compare the Gökçeada, Bozcaada, and Enez study regions and find the optimal area. In the final stage, the areas where offshore wind turbine's locations are calculated are energy and exergy efficiency.

Oman’s 3,165 km coastline presents transformative opportunities for sustainable Blue Economy development, strategically aligned with Oman Vision 2040 and UN SDGs 7, 13, and 14. This study aims to propose a novel AI-enhanced Marine Spatial Planning (MSP) framework to optimize synergies among offshore wind energy, sustainable fisheries, and carbon-neutral port operations. It further seeks to conduct a strategic analysis of these sectors by examining global best practices and Oman specific initiatives. Finally, the research is dedicated to formulating concrete policy recommendations for implementation. This research demonstrates that the proposed AI-driven approach accelerates economic diversification in renewable energy and port logistics, advances climate resilience through port-wind symbiosis reducing CO ₂ emissions, enhances marine conservation via predictive habitat protection, and empowers coastal communities through inclusive governance and increased fisheries productivity collectively advancing Oman’s national development objectives while balancing ecological and economic priorities.