# SeADL: Self-Adaptive Deep Learning for Real-Time Marine Visibility Forecasting Using Multi-Source Sensor Data

## TL;DR

SeADL is a self-adaptive deep learning system that improves real-time marine visibility forecasts using sensor data, enhancing maritime safety in changing ocean conditions.

## Contribution

The novel SeADL framework introduces real-time adaptation for marine visibility forecasting using multi-source sensor data and online learning.

## Key findings

- SeADL achieves high prediction accuracy in marine visibility forecasting.
- The framework maintains robust performance under diverse and extreme environmental conditions.
- Case studies show effective adaptation to both short-term weather and long-term trends.

## Abstract

Accurate prediction of marine visibility is critical for ensuring safe and efficient maritime operations, particularly in dynamic and data-sparse ocean environments. Although visibility reduction is a natural and unavoidable atmospheric phenomenon, improved short-term prediction can substantially enhance navigational safety and operational planning. While deep learning methods have demonstrated strong performance in land-based visibility prediction, their effectiveness in marine environments remains constrained by the lack of fixed observation stations, rapidly changing meteorological conditions, and pronounced spatiotemporal variability. This paper introduces SeADL, a self-adaptive deep learning framework for real-time marine visibility forecasting using multi-source time-series data from onboard sensors and drone-borne atmospheric measurements. SeADL incorporates a continuous online learning mechanism that updates model parameters in real time, enabling robust adaptation to both short-term weather fluctuations and long-term environmental trends. Case studies, including a realistic storm simulation, demonstrate that SeADL achieves high prediction accuracy and maintains robust performance under diverse and extreme conditions. These results highlight the potential of combining self-adaptive deep learning with real-time sensor streams to enhance marine situational awareness and improve operational safety in dynamic ocean environments.