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Advancing Towards a Marine Digital Twin Platform: Modeling the Mar Menor Coastal Lagoon Ecosystem in the South Western Mediterranean

Yu Ye, Aurora González-Vidal, Alejandro Cisterna-García, Angel Pérez-Ruzafa, Miguel A. Zamora Izquierdo, Antonio F. Skarmeta

TL;DR

This paper pioneers the development of a Marine Digital Twin Platform aimed at modeling the Mar Menor Coastal Lagoon Ecosystem in the Region of Murcia by leveraging Artificial Intelligence to emulate complex hydrological and ecological models.

Abstract

Coastal marine ecosystems face mounting pressures from anthropogenic activities and climate change, necessitating advanced monitoring and modeling approaches for effective management. This paper pioneers the development of a Marine Digital Twin Platform aimed at modeling the Mar Menor Coastal Lagoon Ecosystem in the Region of Murcia. The platform leverages Artificial Intelligence to emulate complex hydrological and ecological models, facilitating the simulation of what-if scenarios to predict ecosystem responses to various stressors. We integrate diverse datasets from public sources to construct a comprehensive digital representation of the lagoon's dynamics. The platform's modular design enables real-time stakeholder engagement and informed decision-making in marine management. Our work contributes to the ongoing discourse on advancing marine science through innovative digital twin technologies.

Advancing Towards a Marine Digital Twin Platform: Modeling the Mar Menor Coastal Lagoon Ecosystem in the South Western Mediterranean

TL;DR

This paper pioneers the development of a Marine Digital Twin Platform aimed at modeling the Mar Menor Coastal Lagoon Ecosystem in the Region of Murcia by leveraging Artificial Intelligence to emulate complex hydrological and ecological models.

Abstract

Coastal marine ecosystems face mounting pressures from anthropogenic activities and climate change, necessitating advanced monitoring and modeling approaches for effective management. This paper pioneers the development of a Marine Digital Twin Platform aimed at modeling the Mar Menor Coastal Lagoon Ecosystem in the Region of Murcia. The platform leverages Artificial Intelligence to emulate complex hydrological and ecological models, facilitating the simulation of what-if scenarios to predict ecosystem responses to various stressors. We integrate diverse datasets from public sources to construct a comprehensive digital representation of the lagoon's dynamics. The platform's modular design enables real-time stakeholder engagement and informed decision-making in marine management. Our work contributes to the ongoing discourse on advancing marine science through innovative digital twin technologies.
Paper Structure (19 sections, 7 figures, 2 tables)

This paper contains 19 sections, 7 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related work
  3. Related studies and projects
  4. Research gaps
  5. Methodology
  6. System architecture overview
  7. Data collection
  8. Data interoperability
  9. Models
  10. Minimal predictive models
  11. Run-off what-if scenario: an integrated use case
  12. Mid-term prospects of the digital twin
  13. Data integration
  14. Proposed what-if scenarios
  15. Jellyfish blooms
  16. ...and 4 more sections

Figures (7)

  • Figure 1: Overview of the DT background.
  • Figure 2: Architecture of the digital twin.
  • Figure 3: Overview of the developed DT app, which is divided into three sections. The first one shows the latest real-time data of each location in a 3D map. The second consists of a 2D map where users can visualize last week's data and the output of implemented models. The last one allows users to visualize long-term historical data.
  • Figure 4: Data modeling of SAIH and CTD-IMIDA datasets.
  • Figure 5: Architecture used to deploy the services (with docker). Image extracted from https://github.com/FIWARE/tutorials.Short-Term-History/tree/NGSI-LD
  • ...and 2 more figures