Work

Machine learning-based prediction of passive gears from vessel tracking data in small-scale multi-gear fisheries

Year: 2026

Type: article

Abstract: Small-scale fisheries (SSF) play a crucial role in the Mediterranean Sea, contributing significantly to coastal livelihoods, employment, food security, and local economies. These fisheries are highly diverse and often operate with multiple passive gears within a single trip, targeting different species based on season, market demand, and fisher preference. This gear diversity, combined with the absence of trip-level gear reporting, poses a challenge for accurate monitoring, gear-specific effort estimation, and sustainable management. This study presents a Machine Learning-based approach to predict the type of fishing gear used during individual hauling events from high frequency vessel tracking data.Tracking data were collected from 10 SSF multi-gear vessels based in Ancona (Italy) between January 2023 and March 2024, and over 7,000 hauling events were detected from a total of 1,634 trips. Each event was labelled through fisher validation and expert-informed spatial analysis. Predictive models – Decision Tree, Random Forest, and Extreme Gradient Boosting (XGBo) – were trained and tested using various sets of operational and environmental predictors. Two classification levels were explored: gear categories (nets vs. pots) and specific gear types (i.e., gillnets, trammel nets, and three types of pots).XGBo, using fewer predictors and stricter tuning, demonstrated the best performance, achieving 92% accuracy for gear category and 82% accuracy for specific gear type classification, while remaining less prone to overfitting. The shared and reproducible, hauling event-level approach offers a scalable tool for automated gear classification in multi-gear fisheries and contributes to more precise monitoring, management, and traceability in small-scale coastal systems.

Source: Ecological Informatics

DOI: 10.1016/j.ecoinf.2026.103670

Authors: Pamela Lattanzi, Tania Mendo, Alessandro Galdelli, Anna Nora Tassetti