Generative artificial intelligence and marine ecological monitoring

Year: 2025

Type: article

Abstract: ABSTRACT To meet the needs of the future, marine environmental monitoring must develop methods to efficiently combine and utilise data from a diverse range of sources (e.g., satellite imagery, sensor networks, acoustic data). Generative Artificial Intelligence (GenAI) is uniquely suited to aid with this by enabling the synthesis and integration of heterogeneous and often incomplete data. Its ability to learn underlying statistical patterns supports data fusion, imputation, and enhanced interpretation across sources. GenAI also introduces novel modelling approaches to tackle ecological uncertainties and improve predictive insight. Here, we present a comprehensive overview of GenAI applications in marine ecological monitoring, emphasising its potential to improve data quality control, automate species identification, and support the creation of digital twins. We also highlight key research challenges, such as managing model bias and ensuring system transparency, and outline future directions for integrating GenAI into sustainable marine ecological monitoring and management.

Source: Environmental Modelling & Software

DOI: 10.1016/j.envsoft.2025.106789

Authors: Luciano Ortenzi, Jacopo Aguzzi, Damianos Chatzievangelou, Eugenio Nerio Nemmi, Michele Ferrari, Ivan Masmitja, Morane Clavel-Henry, Nixon Bahamon, Nathan J. Robinson, Giacomo Picardi, Paula Espina, Simona Violino, Riccardo De Angelis, Simone Figorilli, Lavinia Moscovini, Matteo Gallici, Francesca Antonucci, Alessandro Mei, Corrado Costa

Institutions: Università degli Studi della Tuscia, Sapienza University of Rome, Consejo Superior de Investigaciones Científicas, Institut de Ciències del Mar, GEOMAR Helmholtz Centre for Ocean Research Kiel, Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria, Universitat Politècnica de Catalunya

Language: English

Topic: Water Quality Monitoring Technologies

Subfield: Water Science and Technology

Field: Environmental Science

Domain: Physical Sciences

SDG: Life below water

Open Access status: hybrid

Estimated APC paid: $3,500

Funders: Ministerio de Ciencia, Innovación y Universidades, Università degli Studi della Tuscia, European Commission, Horizon 2020 Framework Programme, HORIZON EUROPE Framework Programme, Consejo Superior de Investigaciones Científicas, Agencia Estatal de Investigación, H2020 Marie Skłodowska-Curie Actions, Ministero dell'Istruzione e del Merito

OpenAlex ID: W4416441520