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.
