Splash247: AI weather-routing system cuts coastal pollution from ships

Published by Splash247

Researchers in South Korea have developed an AI-driven ship navigation system that uses real-time weather data to reduce harmful coastal air pollution without sacrificing fuel efficiency.

The new framework, developed by a team at Pusan National University, combines environmental sensor data, physics-informed deep learning and multi-objective optimisation to recommend routes and speed profiles that minimise pollution exposure for communities near ports.

Rather than relying on blanket slow steaming, the system predicts how exhaust plumes will disperse under changing wind and weather conditions, then adjusts a ship’s route and speed to take advantage of more favourable meteorological windows.

“By integrating real-time environmental conditions into navigation decisions, our system recommends optimal routes and speed profiles that reduce pollution reaching coastal communities,” said assistant professor Dowon Kim, who led the study with PhD student Seongbeom Park and professor Jinhyeok Yun.

The researchers argue that the greatest public health risk is often not simply how much a ship emits, but where and when those emissions are transported.

In simulations, including scenarios around Busan Port, the framework improved optimisation performance by 20% to 35% compared with conventional navigation methods, while cutting peak pollutant exposure by 34% to 78%.