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Related Experiment Video

Updated: Jan 29, 2026

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
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SeADL: Self-Adaptive Deep Learning for Real-Time Marine Visibility Forecasting Using Multi-Source Sensor Data.

William Girard1, Haiping Xu1, Donghui Yan2

  • 1Computer and Information Science Department, University of Massachusetts Dartmouth, Dartmouth, MA 02747, USA.

Sensors (Basel, Switzerland)
|January 28, 2026
PubMed
Summary
This summary is machine-generated.

Accurate marine visibility prediction is vital for maritime safety. A new self-adaptive deep learning framework (SeADL) uses real-time sensor data for improved forecasting in challenging ocean conditions.

Keywords:
marine visibility forecastingmaritime safetyonline learningreal-time trainingself-adaptive deep learningtime-series sensor data

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Area of Science:

  • Oceanography
  • Atmospheric Science
  • Artificial Intelligence

Background:

  • Marine visibility prediction is crucial for safe maritime operations, especially in data-sparse and dynamic ocean environments.
  • Traditional deep learning methods face limitations in marine settings due to sparse fixed stations and high spatiotemporal variability.
  • Short-term visibility forecasting improvements can significantly enhance navigational safety and operational planning.

Purpose of the Study:

  • To introduce SeADL, a self-adaptive deep learning framework for real-time marine visibility forecasting.
  • To address the challenges of data scarcity and dynamic conditions in marine visibility prediction.
  • To enhance maritime situational awareness and operational safety through improved forecasting.

Main Methods:

  • Developed SeADL, a self-adaptive deep learning framework.
  • Integrated multi-source time-series data from onboard sensors and drone-borne atmospheric measurements.
  • Implemented a continuous online learning mechanism for real-time model parameter updates.

Main Results:

  • SeADL demonstrated high prediction accuracy in marine visibility forecasting.
  • The framework maintained robust performance across diverse and extreme weather conditions, including storm simulations.
  • Continuous online learning enabled adaptation to both short-term weather fluctuations and long-term environmental trends.

Conclusions:

  • SeADL offers a robust solution for real-time marine visibility forecasting.
  • Combining self-adaptive deep learning with real-time sensor data enhances marine situational awareness.
  • The framework has significant potential to improve operational safety in dynamic ocean environments.