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  11. Predicting Extreme Environmental Values With Hybrid Models: A Perspective Across Air Quality, Wind Energy, And Sensor Networks.
  1. Home
  3. Research Domains
  5. Engineering
  7. Environmental Engineering
  9. Air Pollution Modelling And Control
  11. Predicting Extreme Environmental Values With Hybrid Models: A Perspective Across Air Quality, Wind Energy, And Sensor Networks.

Related Experiment Video

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

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Published on: July 24, 2016

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Predicting Extreme Environmental Values with Hybrid Models: A Perspective Across Air Quality, Wind Energy, and Sensor Networks.

George Efthimiou1

  • 1FLUENC, 55535 Thessaloniki, Greece.

Sensors (Basel, Switzerland)
|November 13, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Hybrid AI models combining physics and data improve extreme environmental predictions. These advanced methods offer faster, more accurate estimates for urban air quality and wind energy, outperforming traditional approaches.

Keywords:
air quality monitoringenvironmental sensor networksextreme value predictionhybrid modeling

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12:44

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Area of Science:

  • Environmental Science
  • Computational Fluid Dynamics
  • Artificial Intelligence

Background:

  • Predicting extreme environmental events is crucial for urban air quality and wind energy.
  • Traditional methods like Computational Fluid Dynamics (CFD) and purely data-driven models have limitations in speed and robustness.

Purpose of the Study:

  • To synthesize recent progress in hybrid approaches for predicting extreme environmental values.
  • To propose a research agenda for advancing these hybrid models by 2030.

Main Methods:

  • Combining empirical formulations, physics-based simulations, and sensor network data.
  • Utilizing hybrid approaches such as physics-informed machine learning, digital twins, and edge AI.
  • Distilling lessons from cross-domain case studies.
wind energy optimization

Main Results:

  • Hybrid models achieve 90-95% accuracy compared to high-fidelity simulations for peak pollutant concentrations and wind speeds.
  • Achieved over 80% reduction in computational cost compared to traditional methods.
  • Demonstrated practical viability for operational use cases.

Conclusions:

  • Hybrid AI approaches offer faster and more robust extreme value predictions than standalone models.
  • Addressing challenges like uncertainty quantification and real-time inference is key for operational deployment.
  • A proposed research agenda focuses on benchmarks, physics-informed AI, and interoperable workflows.