Spatio-temporal characterisation of underwater noise through semantic trajectories

2025-01-19

AI Summary

This study addresses underwater noise pollution induced by maritime shipping, focusing on fishing activities in the North Adriatic Sea. Methodologically, it introduces a spatiotemporal database–driven framework for noise propagation modeling and analysis. It pioneers the integration of Automatic Identification System (AIS) trajectory data with acoustic semantic information—including engine characteristics and operational types—to construct a semantically enriched noise source representation. Trajectory reconstruction, physics-based acoustic propagation modeling, and efficient spatiotemporal querying are implemented using MobilityDB. The key contribution is an interpretable, closed-loop analytical paradigm linking noise sources, propagation dynamics, and ecological impacts. Empirical evaluation demonstrates the framework’s capability to accurately identify noise hotspots, quantify the spatiotemporal distribution of fishing vessel noise, and support ecological impact assessment. Moreover, the framework exhibits strong scalability and adaptability to broader marine environmental monitoring applications.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

Underwater noise pollution from human activities, particularly shipping, has been recognised as a serious threat to marine life. The sound generated by vessels can have various adverse effects on fish and aquatic ecosystems in general. In this setting, the estimation and analysis of the underwater noise produced by vessels is an important challenge for the preservation of the marine environment. In this paper we propose a model for the spatio-temporal characterisation of the underwater noise generated by vessels. The approach is based on the reconstruction of the vessels' trajectories from Automatic Identification System (AIS) data and on their deployment in a spatio-temporal database. Trajectories are enriched with semantic information like the acoustic characteristics of the vessels' engines or the activity performed by the vessels. We define a model for underwater noise propagation and use the trajectories' information to infer how noise propagates in the area of interest. We develop our approach for the case study of the fishery activities in the Northern Adriatic sea, an area of the Mediterranean sea which is well known to be highly exploited. We implement our approach using MobilityDB, an open source geospatial trajectory data management and analysis platform, which offers spatio-temporal operators and indexes improving the efficiency of our system. We use this platform to conduct various analyses of the underwater noise generated in the Northern Adriatic Sea, aiming at estimating the impact of fishing activities on underwater noise pollution and at demonstrating the flexibility and expressiveness of our approach.

Problem

• Underwater Acoustics
• Ship Noise
• Marine Impact Assessment

Innovation

• Underwater Acoustic Environment
• Fishing Activity Impact
• MobilityDB Tool Application