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Visualization, simulation and analyzes of the impact of climate change processes on biofiltration capacity with metaverse technology

ProVerse creates a 3D model of the Danube Delta using real-time data for water quality monitoring. Advanced analytics forecast trends and identify risks. Community platforms engage locals and experts in virtual forums to co-design solutions.

Comprehensive Environmental Monitoring
Predictive Analysis and Forecasting
Community Engagement and Co-Creation

- Lack of comprehensive environmental monitoring: ProVerse's advanced system revolutionizes real-time water quality monitoring by integrating IoT with sophisticated digital twin technology. This setup enables a nuanced ecological analysis with unparalleled detail and accuracy. By capturing and synthesizing diverse data streams, our solution offers an in-depth view of the ecosystem's health, supporting informed environmental stewardship and proactive management decisions.
- Lack of predictive analysis and comprehensive forecasting: ProVerse's solution integrates state-of-the-art AI and machine learning to pioneer predictive modelling for environmental trends. This approach not only forecasts ecological changes with high accuracy but also empowers proactive ecosystem management and targetedconservation strategies. By anticipating environmental shifts, our technology plays a crucial role in sustaining ecosystem health and resilience.
- Challenges in community engagement: ProVerse is dedicated to forging strong connections between local communities, scientists, and policymakers through our interactive platforms. These platforms are designed to foster collaborative innovation and shared knowledge, ensuring that the solutions developed are not only inclusive but also deeply rooted in the specific needs and insights of the Danube Delta region. This approach guarantees that environmental initiatives are both culturally sensitive and highly effective, aligning with local realities and aspirations. Hazard creates river floods, heavy precipitation / pluvial floods, and sea level rise.

TRL4. Laboratory testing of prototype component or process

See more information about this level and the TRL and SRL levels.

The main components of the system have been tested separately, and an initial integration exercise has been conducted.

Credits
CREDITS
Chief Executive Officer - Maarit Pihl
Collaborators
COLLABORATORS
No collaborator

How does it work?

ProVerse’s system synthesizes environmental data from real-time sensors, satellite imagery, and historical records for the Danube Delta. This integrated approach provides a comprehensive ecological understanding, vital for effective ecosystem management. By combining diverse data streams, we offer deeper insights and accurate predictions.

Our custom machine learning models are tailored to the Danube Delta’s unique ecology, ensuring precise environmental assessments and interventions. These AI-driven insights lead to effective and sustainable management strategies.

ProVerse also integrates virtual environments for community involvement in conservation. These platforms offer immersive experiences, fostering greater social engagement and enriching stakeholder dialogue, ensuring inclusive and shared ecosystem management.

ProVerse’s innovation was developed as part of a comprehensive R&D project, combining in-house expertise with strategic partnerships. Our team, comprising experts in digital technologies, and data analytics, focused on creating solutions specifically tailored for environmental monitoring and management. Collaborations with academic institutions and environmental agencies provide valuable insights and access to cutting-edge research, ensuring our solutions are grounded in scientific rigor. These partnerships also facilitated the integration of community perspectives, vital for the co-creation and acceptance of our environmental management tools.

ProVerse’s innovation performs optimally in areas with good technological infrastructure. While our systems are highly effective, their performance might be slightly constrained in regions with limited connectivity or underdeveloped tech resources. Additionally, the accuracy of our predictive models is most robust when applied to environments well-represented in existing data. Our community engagement platforms are most effective when there is a high level of local involvement and digital literacy.

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