http://dspace.azti.es/handle/24689/2 2026-04-21T10:45:44Z http://dspace.azti.es/handle/24689/2785 Título : Evolutionary multi-objective fishing routing with decision maker's preferences Autor : Granado, Igor; Szlapczynska, Joanna; Szlapczynski, Rafal; Hernando, Leticia; Fernandes-Salvador, Jose A. Resumen : This study aims to enhance economic and environmental sustainability of fisheries through fishing routing methods that can reduce operational costs, emission footprints, and incidental fishing risks. To achieve this, a novel problem definition is introduced, the time-dependent multi-objective orienteering problem with time windows and moving targets (TDMOOP-TWMT). Unlike existing fishing routing problems, the TDMOOP-TWMT allows users to define their fishing trips by setting a maximum time at sea rather than a predefined number of fishing sets. This multi-objective problem includes three goals: fuel-oil consumption, catches of tuna species, and incidental catches of non-target species (bycatch). To address this problem, the w-MOEA/D algorithm is employed, which incorporates decision-makers' preferences using wide weight intervals for each objective, eliminating the need for precise weight values. Compared to the classical MOEA/D, the w-MOEA/D method achieves solutions closer to the true Pareto front while reducing the final solution set based on users' preferences. To demonstrate the potential application and benefits in a real context, 12 historical routes are employed across different fishing scenarios, each defined by varying the weight intervals of the objectives. The results show that w-MOEA/D routes allow for consuming less fuel and catching more tuna, though with a higher risk of bycatch when compared to historical trips. However, prioritizing bycatch avoidance reduces this risk while maintaining similar fuel efficiency, although with a lower increase in catches. In summary, this study highlights the effectiveness of the proposed solution method in supporting fishers' decision-making by incorporating their preferences when planning fishing routes. 2025-06-30T00:00:00Z http://dspace.azti.es/handle/24689/2780 Título : Morphodynamic predictions based on Machine Learning. Performance and limits for pocket beaches near the Bilbao port Autor : Vines, Manuel; Sanchez-Arcilla Jr, Agustin; Epelde, Irati; Mosso, Cesar; Franco, Javier; Sospedra, Joaquim; Abalia, Aritz; Liria, Pedro; Grifoll, Manel; Ojanguren, Alberto; Hernaez, Mario; Gonzalez, Manuel; Sanchez-Arcilla, Agustin Resumen : Predicting the morphodynamic behaviour of pocket beaches exposed to energetic waves and meso-tidal ranges-particularly under strong seasonal variability and the influence of climate change-requires a robust characterization of coastal morphodynamics across a wide range of temporal and spatial scales. This study introduces a data-driven modelling approach using Machine Learning (ML), specifically the Gradient Boosting Regressor (GBR), a powerful ensemble technique capable of iteratively improving predictions from limited datasets. The GBR model is applied to forecast beach evolution in complex coastal settings, where physical understanding is limited, specifically targeting a set of pocket beaches in the Bay of Biscay (North Atlantic). The methodology combines wave time series and morphodynamic variables obtained through videometry stations (KOSTASystem technology). This ML framework is then implemented to improve the current understanding of hydro-morphological interactions and establish criteria to enhance the reliability of erosion and flood predictions. The obtained predictions can steer the design and implementation of protection measures to increase beach resilience under climate change drivers, such as sea-level rise and wave storminess, leading to improved adaptation strategies. This approach, which also demonstrates the advantages of ML over conventional statistics, is developed from a set of extreme meteo-oceanographic events acting on pocket beaches adjacent to and within the Nervi & oacute;n estuary and Bilbao port. The application of conventional statistics and ML techniques to this dataset begins with an extreme analysis of offshore wave data, from which a set of 32 wave storms has been propagated towards the coast using the Simulated WAves Nearshore (SWAN) model. This dataset serves to evaluate predictive formulations derived from statistical and ML tools, based on monthly values, which filter out short-term variability and focus on medium- to long-term (annual to decadal) beach behaviour-scales that are critical for sustainable coastal management. Results demonstrate that ML-based predictions using GBR outperform traditional statistical methods, where validation metrics confirm the improved predictive accuracy, with R2 values exceeding 0.7 in several cases, without any evidence of overfitting. These predictions contribute to understanding hydro-morphological interactions and support the design of adaptive beach protection strategies. 2025-01-01T00:00:00Z http://dspace.azti.es/handle/24689/2781 Título : First documentation of putative mating behavior in blue sharks (Prionace glauca) reveals a potential reproductive area in the Northeast Atlantic Autor : Vossgaetter, L.; Müller, L.; Cruz, I.; Schultz, M.; Renner, A.; Erauskin-Extramiana, M. Resumen : Reproductive behavior in sharks remains poorly understood, with direct observations of mating reported in only a few species. The blue shark (Prionace glauca) is a widely distributed, placental viviparous species, yet direct evidence of mating behavior remains undocumented. Here, we describe the first visual documentation of a putative mating attempt involving blue sharks in the Bay of Biscay, off the Basque coast, observed during a shark ecotourism dive in July 2023. An adult male and an immature female exhibited a sequence of behaviors consistent with shark courtship, including parallel swimming, following, a courtship bite, and an inversion of both individuals. Additionally, we documented females with mating scars across 4 consecutive years, the majority of which were considered immature. These observations align with prior reports suggesting mating attempts between adult males and immature females. The combination of direct behavioral observation and repeated evidence of mating scars highlights the potential reproductive significance of the region and underscores the need for further research on the demographics, habitat use, and reproductive ecology of blue sharks in the Northeast Atlantic. 2025-01-01T00:00:00Z http://dspace.azti.es/handle/24689/2782 Título : UCODENs: Underwater Collaborative Detection Networks Autor : Xu, J.; Kishk, M. A.; Irigoien, X.; Alouini, M. S. 2025-01-01T00:00:00Z