A Scalable Model for Vessel-Generated Underwater Noise: Enhancing Efficiency through Parallelisation

Underwater noise pollution by shipping activities is widely recognised as a significant threat to marine life. The noise emitted by vessels can have various detrimental effects on fish and marine ecosystems. Therefore, accurately estimating and analysing vessel-generated underwater noise is a critical challenge for the protection and conservation of marine environments. For this reason, we have built a model for the spatio-temporal characterisation of underwater noise generated by vessels. This paper builds on this model by optimising the code pipeline, implementing table partitioning and leveraging parallelisation techniques. These enhancements allow us to explore various partitioning methods while significantly improving the computational performance and enabling more efficient analysis of underwater noise. Our approach not only improves the computational efficiency but also preserves the accuracy of the noise calculations, offering a more scalable solution for large datasets.