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Applying Automatic Identification System Data to Determine Dredging Production Rates and Resulting Sediment Plume Compliance – a Port Everglades Case Study

€ 20,-


Presented during:

WODCON XXIII - Dredging is changing - The Practice. The Science. The Business.


M. Bell, J. van Berkel, T. Foster


"Daily sediment plume hindcast modelling provides essential insight into dredging operations, as documented in international guidelines such as, PIANC 108-2010 and can be used to manage project compliance with respect to suspended sediment concentrations (SSC), sedimentation and habitat impact targets. For some dredging projects, depending on contractual obligations, daily production information from dredging contractors may not be available for environmental management purposes. This has often been seen as a hindrance to the use of daily sediment plume hindcast techniques in the environmental management of dredging projects. The presented paper illustrates how dredge production rates, at a level of reliability suitable for sediment plume hindcast modelling, can be derived based on Automatic Identification System (AIS) data and basic understanding of the involved dredge equipment. In the presented case study of maintenance dredging at Port Everglades demonstrates that, despite no involvement in the project, various data science techniques can be used to determine the number of vessels involved in the dredging operation, their operating locations and estimates of daily production. The resulting dredge operation schedule was implemented in a MIKE 21 FM HD/MT model that quantified the potential sediment plume (SSC and sedimentation) impacts from the maintenance dredging works without requirement for specific information from the dredge contractor. While best practices for environmental management of dredging emphasize, among many other factors, transparency of production information. This case study provides evidence that alternative solutions for attaining suitably robust production data, using publicly available satellite AIS information, are practical and accessible to regulators and third parties (NGOs etc.). This facilitates the application of hindcast modelling, to strengthen environmental management activities, regardless of disclosure of production data by the dredging contractor."

Keywords: Sediment transport modelling, Automatic Identification System (AIS), international best practices for environmental management, sediment plumes, data science



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