Artificial Intelligence in Port Environmental Management: A Strategic Analysis

Abstract

Maritime activities play a crucial role in global trade. However, seaports cause various environmental problems, particularly pollution in coastal and urban areas. Artificial intelligence (AI) and its subfields, machine learning and deep learning have emerged as promising tools for addressing these problems, garnering increasing interest within the maritime sector. Nevertheless, existing studies in literature often focus on a limited scope and fail to incorporate environmental priorities in seaport operations. This study explored the potential of AI and its subfields to enhance resilience to environmental problems posed by operational activities in seaports. Ten port environmental priorities from the ESPO’s report were included as environmental indicators. The study was conducted in two phases. The first phase involved a systematic literature review of 117 sources from the Web of Science and Scopus databases. In line with the systematic analysis, the second phase was evaluated using a SWOT analysis. Thereafter, a series of strategic recommendations were formulated on the based on an analysis of both internal and external factors. The study provided twelve strategic recommendations for enhancing current practices. AI and its subfields has the potential to become a strategic tool for achieving seaport sustainability goals that align with environmental priorities.

Keywords

• Artificial Intelligence
• Machine Learning
• Deep Learning
• Environmental Priority
• Seaport Management
• ESPO

Liman Çevre Yönetiminde Yapay Zeka: Stratejik Bir Analiz

Abstract

Denizcilik faaliyetleri küresel ticarette çok önemli bir rol oynamaktadır. Ancak limanlar, özellikle kıyı ve kentsel alanlarda kirlilik olmak üzere çeşitli çevre sorunlarına neden olmaktadır. Yapay zekâ (YZ) ve alt alanları olan makine öğrenimi ve derin öğrenme, bu sorunları çözmek için umut vaat eden araçlar olarak ortaya çıkmış ve denizcilik sektöründe giderek artan bir ilgi görmüştür. Bununla birlikte, literatürdeki mevcut çalışmalar genellikle sınırlı bir alana odaklanmakta ve liman operasyonlarında çevresel öncelikleri dikkate almamaktadır. Bu çalışma, deniz limanlarındaki operasyonel faaliyetlerin yol açtığı çevresel sorunlara karşı dayanıklılığı artırmak için YZ ve alt alanlarının potansiyelini araştırmıştır. ESPO raporundaki on liman çevresel önceliği çevresel göstergeler olarak dahil edilmiştir. Çalışma iki aşamada gerçekleştirilmiştir. İlk aşamada, Web of Science ve Scopus veritabanlarından 117 kaynağın sistematik bir literatür taraması yapılmıştır. Sistematik analizle uyumlu olarak, ikinci aşama SWOT analizi kullanılarak değerlendirilmiştir. Ardından, iç ve dış faktörlerin analizine dayalı olarak bir dizi stratejik öneri formüle edilmiştir. Çalışma, mevcut uygulamaları geliştirmek için on iki stratejik öneri sağlamıştır. YZ ve alt alanları, çevresel önceliklerle uyumlu liman sürdürülebilirlik hedeflerine ulaşmak için stratejik bir araç olma potansiyeline sahiptir.

Keywords

• Yapay Zekâ
• Makine Öğrenimi
• Derin Öğrenme
• Çevresel Öncelik
• Liman Yönetimi
• ESPO

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