Dredging is widely used in coastal areas for the maintenance of port navigation, land reclamation, beach nourishment, and so on. However, the growth of environmental awareness places lots of restrictions on coastal hydraulic infrastructure construction since the subsequent dredging plumes can reduce the visibility and light penetration, affect the growth of the photosynthesis, re-suspend the absorbed contaminants, destroy the marine habitats, among other undesirable consequences. In this context, the assessment of the environmental and socio-economic risk induced by dredging is a fundamental step during both the design stage and the operational management.

Traditional risk assessment strategies typically use qualitative or semi-quantitative approaches, relying heavily on numerical simulations of specific events or site observations, which makes them less adaptable to emerging or unknown risks. To overcome those drawbacks, in this study, a novel risk assessment framework based on computational fluid dynamics (CFD) regarding dredging processes was introduced considering coastal circulations, sediment transport, and environmental/socio-economic vulnerabilities. In addition, this framework was equipped with an unsupervised machine learning clustering algorithm, K-means clustering, for generating representative meteocean scenarios subsequently used to force a regional circulation model. The database of featured hydrodynamic conditions was independent of particular climate events and possessed the frequency of meteocean scenarios, which was crucial in quantifying the overall risk and predicting high-risk areas.

Two original visualization methods were proposed accompanied by the above risk assessment framework, which are risk boxes and risk roses. The risk boxes were used to describe the risk data by demonstrating the locality, spread, and skewness groups from a spatial perspective. The risk roses, inspired by the wind rose, linked the risk with the wind conditions, i.e. scenario based. By these two means, it was easy to identify the risky areas and critical climate conditions.
This risk assessment framework has been tested in Pearl River Estuary – Hong Kong waters. Hydraulic and mechanical dredging were considered, as well as different dredging locations and duty cycles of dredgers. In addition, this framework was expanded to estimate the risk of re-suspension of Liquid Crystal Monomers (LCM) and the dredging risk on the habitat of the humpback dolphins. The suggested framework can be considered a step forward in the environmental and socio-economic impact of the dredging operations and could support the design stage and the decision-making process during the dredging operations.

Dredging,Risk assessment,Computational fluid dynamics (CFD),Clustering,Marine environment