Short-Term Optimal Ship Routing via Reliable Satellite Current Data

Artemis Ioannou, Evangelos Moschos, B. Le Vu, A. Stegner
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Abstract

Optimal ship routing systems require highly accurate oceanic data. Our technological innovation is based on the use of high-resolution currents derived from the fusion of various satellite observations by harnessing Artificial Intelligence methods. Today, routing strategies rely mainly on the outputs of operational oceanic models that cannot always guarantee the accurate prediction of surface currents. In this study we compare our HIRES currents data stemming from satellites, with commonly used operational oceanic models, reducing errors by more than a factor of two, both for a nowcast and short forecast scenarios. We explore a specific optimization example along a highly commercial shipping road in the eastern Mediterranean Sea, demonstrating the advantage of our method. We show that high reliability on the observed oceanic conditions allows for a short-term oceanic routing that can significantly optimize the ship’s voyage time as well as the ship’s fuel consumption. This low-cost/low-risk solution can be employed today to advance shipping decarbonization.
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基于可靠卫星电流数据的短期最优船舶航线
最佳的船舶航线系统需要高度精确的海洋数据。我们的技术创新是基于利用人工智能方法融合各种卫星观测数据而产生的高分辨率电流。今天,路线策略主要依赖于操作海洋模型的输出,这些模型不能总是保证对表面洋流的准确预测。在本研究中,我们将来自卫星的HIRES电流数据与常用的业务海洋模型进行了比较,将临近预报和短期预报情景的误差减少了两倍以上。我们探索了地中海东部一条高度商业航运道路的具体优化示例,展示了我们方法的优势。我们表明,在观察到的海洋条件下,高可靠性允许短期海洋航线,可以显着优化船舶的航行时间以及船舶的燃料消耗。这种低成本/低风险的解决方案可以在今天用于推进航运脱碳。
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