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Full Description
Machine learning and data analytics can be used to inform technical, commercial and financial decisions in the maritime industry. Applications of Machine Learning and Data Analytics Models in Maritime Transportation explores the fundamental principles of analysing maritime transportation related practical problems using data-driven models, with a particular focus on machine learning and operations research models.
Data-enabled methodologies, technologies, and applications in maritime transportation are clearly and concisely explained, and case studies of typical maritime challenges and solutions are also included. The authors begin with an introduction to maritime transportation, followed by chapters providing an overview of ship inspection by port state control, and the principles of data driven models. Further chapters cover linear regression models, Bayesian networks, support vector machines, artificial neural networks, tree-based models, association rule learning, cluster analysis, classic and emerging approaches to solving practical problems in maritime transport, incorporating shipping domain knowledge into data-driven models, explanation of black-box machine learning models in maritime transport, linear optimization, advanced linear optimization, and integer optimization. A concluding chapter provides an overview of coverage and explores future possibilities in the field.
The book will be especially useful to researchers and professionals with expertise in maritime research who wish to learn how to apply data analytics and machine learning to their fields.
Contents
Chapter 1: Introduction of maritime transportation
Chapter 2: Ship inspection by port state control
Chapter 3: Introduction to data-driven models
Chapter 4: Key elements of data-driven models
Chapter 5: Linear regression models
Chapter 6: Bayesian networks
Chapter 7: Support vector machine
Chapter 8: Artificial neural network
Chapter 9: Tree-based models
Chapter 10: Association rule learning
Chapter 11: Cluster analysis
Chapter 12: Classic and emerging approaches to solving practical problems in maritime transport
Chapter 13: Incorporating shipping domain knowledge into data-driven models
Chapter 14: Explanation of black-box ML models in maritime transport
Chapter 15: Linear optimization
Chapter 16: Advanced linear optimization
Chapter 17: Integer optimization
Chapter 18: Conclusion
