A scalable AIS-based model for vessel-generated underwater noise

Underwater noise pollution from shipping activities is widely recognised as a significant threat to marine life. Noise emitted by vessels can have various detrimental effects on fish and marine ecosystems. Accurately estimating and analysing vessel-generated underwater noise is therefore of critical importance for the protection and conservation of marine environments. In this paper, we present an enhanced version of our model for the spatiotemporal characterisation of vessel-generated underwater noise, with a focus on improving its scalability. The original model was limited to fishing vessels and relied on Automatic Identification System (AIS) data to reconstruct trajectories, as well as engine horsepower to estimate emitted noise. Here, we generalise the approach to include all vessel categories — including tankers, cruise ships, and recreational boats — still relying on AIS data, but estimating noise as a function of vessel length overall (LOA) and category, since horsepower information is not available for all vessels in the dataset. We broaden the study area to include the Central Adriatic Sea, in addition to the Northern part previously considered. The enlarged area and the substantially greater volume of AIS data introduce significant computational challenges, making scalability a primary concern. We address these challenges through a comprehensive analysis of optimisation strategies to improve query execution performance. In particular, we restructure the computational pipeline by implementing table partitioning and leveraging parallelisation techniques. Specifically, we employ PostgreSQL’s native parallel query execution and implement multiple partitioning strategies, including range, hash, and list partitioning. We further explore spatial partitioning through space tiling, comparing regular, adaptive, and k-d tree-based grids. Finally, we leverage the Citus extension to distribute computation across four and eight nodes. Our approach improves computational efficiency while preserving the accuracy of noise calculation, offering a scalable solution for large datasets.