SWR-Viz: AI-assisted Interactive Visual Analytics Framework for Ship Weather Routing
This work addresses the problem of operational adoption of weather routing for maritime transport by providing a practical decision-support system, though it is incremental as it integrates existing methods like Fourier Neural Operators and SIMROUTE with visual analytics.
The paper tackles the challenge of efficient maritime transport by developing SWR-Viz, an AI-assisted visual analytics framework that combines physics-informed wave forecasting with routing and emissions analytics, showing improved forecast stability and realistic routing outcomes comparable to ground-truth data in key shipping corridors.
Efficient and sustainable maritime transport increasingly depends on reliable forecasting and adaptive routing, yet operational adoption remains difficult due to forecast latencies and the need for human judgment in rapid decision-making under changing ocean conditions. We introduce SWR-Viz, an AI-assisted visual analytics framework that combines a physics-informed Fourier Neural Operator wave forecast model with SIMROUTE-based routing and interactive emissions analytics. The framework generates near-term forecasts directly from current conditions, supports data assimilation with sparse observations, and enables rapid exploration of what-if routing scenarios. We evaluate the forecast models and SWR-Viz framework along key shipping corridors in the Japan Coast and Gulf of Mexico, showing both improved forecast stability and realistic routing outcomes comparable to ground-truth reanalysis wave products. Expert feedback highlights the usability of SWR-Viz, its ability to isolate voyage segments with high emission reduction potential, and its value as a practical decision-support system. More broadly, this work illustrates how lightweight AI forecasting can be integrated with interactive visual analytics to support human-centered decision-making in complex geospatial and environmental domains.