Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-18T07:54:28.919Z Has data issue: false hasContentIssue false
  • English
  • Français

Research on the hydrographic survey cycle for updating navigational charts

Published online by Cambridge University Press:  21 January 2021

Jing Duan ,
Xiaoxia Wan  and
Jianan Luo Open the ORCID record for Jianan Luo [Opens in a new window]
Jing Duan
Affiliation:
School of Printing and Packaging, Wuhan University, Wuhan, China
Xiaoxia Wan*
Affiliation:
School of Printing and Packaging, Wuhan University, Wuhan, China
Jianan Luo
Affiliation:
School of Printing and Packaging, Wuhan University, Wuhan, China
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Due to the vast ocean area and limited human and material resources, hydrographic survey must be carried out in a selective and well-planned way. Therefore, scientific planning of hydrographic surveys to ensure the effectiveness of navigational charts has become an urgent issue to be addressed by the hydrographic office of each coastal state. In this study, a reasonable calculation model of hydrographic survey cycle is established, which can be used to make the plan of navigational chart updating. The paper takes 493 navigational charts of Chinese coastal ports and fairways as the research object, analyses the fundamental factors affecting the hydrographic survey cycle and gives them weights, proposes to use the BP neural network to construct the relationship between the cycle and the impact factors, and finally establishes a calculation model of the hydrographic survey cycle. It has been verified that the calculation cycle of the model is effective, and it can provide reference for hydrographic survey planning and chart updating, as well as suggestions for navigation safety.

Keywords

navigational charthydrographic surveyupdatecycle
Type
Research Article
Information
The Journal of Navigation , Volume 74 , Issue 4 , July 2021 , pp. 750 - 762
Check for updates
Copyright
Copyright © The Royal Institute of Navigation 2021

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Christine, S. (2015). Crowdsourced Maritime Data: Examining the feasibility of using under keel clearance data from AIS to identify hydrographic survey priorities. Master degree, Faculty of the USC Graduate School University of Southern California, America.Google Scholar
CHS (Canadian Hydrographic Service). (2013). Available at: http://www.charts.gc.ca.Google Scholar
CNKI Knowledge. (2018).Available at: http://xuewen.cnki.net.Google Scholar
Freedman, D. and Mo, C. (1983). Root mean square error of regression. Mathematical Statistics and Management, (4), 25–30.Google Scholar
Gan, C. H. (2014). Research on Seamless Multi-Scale Electronic Navigation Charts Data Organization and Its Application. Ph.D. degree, Wuhan University, China.Google Scholar
Gao, D. Q. (1998). On structures of supervised linear basis function feedforward three-layered neural networks. Chinese Journal of Computers, 21(1), 80–86.Google Scholar
IHB (International Hydrographic Bureau). (2017). International Hydrographic Review. Monaco: International Hydrographic Organization.Google Scholar
Kung, S. Y. and Hwang, J. N. (1988). An algebraic projection analysis for optimal hidden units size and learning rates in back-propagation learning. IEEE International Conference on Neural Networks, 1, 363–370.10.1109/ICNN.1988.23868CrossRefGoogle Scholar
Li, J. K., Zhang, J., Gong, L. T. and Miao, P. (2015). Research on the total factor productivity and decomposition of Chinese coastal marine economy: based on DEA-Malmquist index. Journal of Coastal Research, 73, 283–289.10.2112/SI73-050.1CrossRefGoogle Scholar
Ling, Y., Xu, G. J., Lu, X. P., Wang, R. and Su, Z. L. (2002). Research on bathymetric survey cycle. Hydrographic Surveying and Charting, 22(3), 22–25.Google Scholar
Liu, Z. (2016). Research on Risk Evaluation System for Vessels in Yellow Sea and Bohai Sea during Severe Weather. Master of Engineering, Dalian Maritime University, China.Google Scholar
Liu, J. Y. and Xia, F. (2013). Statistical analysis of benefit from regulation engineering in the Yangtze estuary deepwater channel. Port & Waterway Engineering, 11, 29–32.Google Scholar
Michelle, G. and Patrick, H. (1991). Model for Determination of Cartographic and Hydrographic Priorities. Canadian Hydrographic Conference. Canada.Google Scholar
MMO (Marine Management Organization). (2013). Available at: http://www.marinemanagement.org.uk.Google Scholar
MSA(Maritime Safety Administration). (2017). Planning Catalogue of Port and Fairway Charts (Catalogue).Google Scholar
NOAA (National Oceanic and Atmospheric Administration). (2012). Available at: http://www.noaa.gov.Google Scholar
Saaty, T. L. (1994). Highlights and critical points in the theory and application of the Analytic Hierarchy Process. European Journal of Operational Research, 74(3), 426–447.10.1016/0377-2217(94)90222-4CrossRefGoogle Scholar
Sadeghi, B. H. M. (2000). A BP-neural network predictor model for plastic injection molding process. Journal of Materials Processing Technology, 103(3), 411–416.10.1016/S0924-0136(00)00498-2CrossRefGoogle Scholar
Sang, G. K., Won, I. L. and Yang, H. W. (2015). An analysis on relative importance and priority of hydrographic survey for major ports in South Korea. Journal of the Korean Society of Marine Environment & Safety, 21(2), 154–163.Google Scholar
Shi, F., Wang, X. C., Yu, L., Li, Y. and Zhang, Y. L. (2010). 30 Cases Analysis of MATLAB Neural Network. Beijing: Beihang University Press.Google Scholar
Tsubakimoto, T. (2000). Extensions of the analytic hierarchy process: analytic hierarchy process, analytic network process, and neural network process. Kwansei Gakuin Shogaku Kenkyu, 46, 79–102.Google Scholar
Zhang, L. (2018). Forge development synergy across land and over sea. Maritime China, 0(1), 56–57.Google Scholar
Zhang, X. J. and Cao, H. R. (2010). The random function in Matlab. Artificial Intelligence and Identification Techniques, 14(2), 115–116.Google Scholar
Zhang, S. Y. and Chen, H. (2009). Discussion of safe navigation in the winter North-south Route. Ship & Ocean Engineering, 38(1), 71–73.Google Scholar
Zhang, J. R. and Lok, T. M. (2007). A hybrid particle swarm optimisation–back-propagation algorithm for feedforward neural network training. Applied Mathematics & Computation, 185(2), 1026–1037.10.1016/j.amc.2006.07.025CrossRefGoogle Scholar