SWR-Viz: AI-assisted Interactive Visual Analytics Framework for Ship Weather Routing
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917092196876288 |
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| author | Hazarika, Subhashis Lupin-Jimenez, Leonard Vuppala, Rohit Chattopadhyay, Ashesh Wong, Hon Yung |
| author_facet | Hazarika, Subhashis Lupin-Jimenez, Leonard Vuppala, Rohit Chattopadhyay, Ashesh Wong, Hon Yung |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_15182 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SWR-Viz: AI-assisted Interactive Visual Analytics Framework for Ship Weather Routing Hazarika, Subhashis Lupin-Jimenez, Leonard Vuppala, Rohit Chattopadhyay, Ashesh Wong, Hon Yung Human-Computer Interaction Artificial Intelligence 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. |
| title | SWR-Viz: AI-assisted Interactive Visual Analytics Framework for Ship Weather Routing |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2511.15182 |