Screen LibraryApplications of machine learning and data analytics models in maritime transportation /
"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 relat...
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| Main Authors: | |
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| Group Author: | |
| Published: |
The Institution of Engineering and Technology,
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| Publisher Address: | Stevenage, Herts, United Kingdom : |
| Publication Dates: | 2022. |
| Literature type: | Book |
| Language: | English |
| Series: |
IET transportation series ;
38 |
| Subjects: | |
| Summary: |
"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."-- |
| Carrier Form: | viii, 305 pages : illustrations (some color) ; 25 cm. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: |
9781839535598 1839535598 |
| Index Number: | HE571 |
| CLC: | U6-39 |
| Call Number: | U6-39/Y21 |
| Contents: | Chapter 1: Introduction of maritime transportationChapter -- 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. |