1st Edition

Information Engineering for Ports and Marine Environments

By Lin Mu, Lizhe Wang, Mingwei Wang Copyright 2020
    254 Pages 246 B/W Illustrations
    by Chapman & Hall

    254 Pages 246 B/W Illustrations
    by Chapman & Hall

    Information Engineering for Port and Marine Environments provides the technology of tidal level prediction, the technology of oil spill early-warning, and the research for the theory of storm sedimentation, the construction for monitor ability, the early-warning service for numerical simulation and operational, which involves many aspects such as theoretical research, system establishment, and application of information technology, et al. Because of the certain prospective and advancement of multiple work, it will play a positive role in promoting the related technology of the field.

    There are several of important offshore ports in China, such as Tianjin port, Yangshan Port, Ningbo-Zhoushan port, Huanghua port et al., most of them are located in the coast of muddy and muddy silty, and the depth of water is shallow, the sediment deposition is serious, the large ship is operated by tide. In order to sufficiently keep the rapid and stable economic growth in bay, estuary and delta, guarantee the security of port, channel, maritime, oceanic engineering and resource development of oil and gas, and better escort for the social economy activities, it is essential to provide the information service of sediment and ocean hydrometeorology with width coverage, and forecasting and warning information.

    It is all the latest research results in the book, which involves many fields such as physical oceanography, meteorology, biology, chemistry, geology, environment, transportation and law and so on. The development of information assurance and prediction system for port shipping and ocean environment is a huge and arduous project. It is too hasty to finish the book, due to the limited knowledge of the author, the careless is unavoidable, cordially invites the readers to point out.


    • An entire system to forecast the port shipping and ocean environment information is proposed, including what is the port shipping and ocean environment information.

    • The concept of port shipping and ocean environment data integration is presented, and the essential modules are built for the ocean dynamics model.

    • The high performance port shipping and ocean environment data processing system is constructed, and the model dataset and geographic information is obtained to build the basic database. 
    • The application of information assurance technology for port shipping and ocean environment is conducted at Tianjin port and Yangshan Port.

    This book is meant for senior undergraduates and postgraduate students in the fields of geoinformatics, Port engineering and Marine engineering. Engineers and technicians in the related fields can also use it for reference.

    Author Bios. Foreword. Preface. Acknowledgment. Key Issues in Marine Environment Information Support Technology. Fundamentals of Fluid Mechanics and Marine Dynamics. Modelling. Forecasting Technology of Tide-bound Water Level. Key Technology of Oil Spill Early-warning and Forecasting. Forecasting Technology of Sediment Transport. Research of Integrated Marine Environment Information Support and Forecast System. Case of Application at Tianjin Port. Case of Application at Yangshan Port. Bibliography. Index.


    Lin Mu is a professor and doctoral supervisor of College of Life Sciences and Oceanography, Shenzhen University. He was born in 1977, received Ph.D. degree from Ocean University of China and majored in physical oceanography. Dr. Mu has been devoted to the research fields of informational maritime safety support and applied oceanography, and has obtained significant achievements in recent years. He has published 3 monographs and over 50 research papers, 20 of which are covered by Science Citation Index. As the chief scientist, he has been presiding important projects such as National Key Research and Development Program of China, National Natural Science Foundation Project of China and National Science and Technology Support Program of China. In the field of informational maritime safety support, Prof. Mu is specialized in marine oil-spill pollution warning and firstly developed a prediction and warning system of marine oil-spill, search and rescue integration in China, which has been used successfully in a series of accident issues. As an expert in marine search and rescue techniques, he  studied, predicted and analyzed the drifting trajectory of the debris of Flight MH370, which provided technical
    support for related emergency responses. In the field of applied oceanography, Prof. Mu proposed a real-time tidal level prediction system based on statistics and dynamic model by coupling the real-time monitoring data of meteorology and tidal level, statistical prediction method of tide, atmospheric and marine dynamics model, which conquered the drawbacks of traditional models and brought clear economic benefits.

    Lizhe Wang is a “ChuTian” Chair Professor at School of Computer Science, China University of Geosciences (CUG), and a Professor at Institute of Remote Sensing & Digital Earth, Chinese Academy of Sciences (CAS). Prof. Wang received B.E. & M.E from Tsinghua University and Doctor of Engineering from University Karlsruhe (Magna Cum Laude), Germany. Prof. Wang is a Fellow of IET, Fellow of British Computer Society. Prof. Wang serves as an Associate Editor of IEEE TPDS, TCC and TSUSC. His main research interests include HPC, e-Science, and remote sensing image processing.

    Mingwei Wang received his Ph.D. degree from Wuhan University, Wuhan, China, in 2018. He obtained the B.S. and M.S. degrees from Hubei Normal University, Huangshi, China, in 2011 and Hubei University of Technology, Wuhan, China, in 2015. He is currently a full-time scientific researcher in School of Geological Survey, China University of Geosciences, Wuhan, China. His major research interests include remote sensing image processing, swarm intelligence and machine learning.