Maritime software company Nautilus Labs has released a White Paper comparing the impact of different data source inputs on simulation accuracy rates in performance and voyage optimisation models in the shipping sector.
The study evaluated data science metrics that measure the accuracy of simulations built with three types of data sets: models based on noon reports only; models based on high-frequency sensor data; and models based on a combination of a vessel’s noon reports combined with high-frequency sensor data from other similar vessels.
The paper found that, while simulations built on high-frequency sensor data yield the most accurate simulations, in situations where a vessel is not equipped with sensors, simulation accuracy can be significantly improved by feeding the underlying model with a combination of data from the vessel’s noon reports and sensor data from similar vessels. Models based only on noon reports yielded the least accurate predictions.
The research found that noon report data combined with data derived from similar vessels within Nautilus’s own data pool provided 62% of the benefits of high frequency data from the ship itself.
“While high-frequency sensors are the gold standard in data collection for seafaring vessels, the reality is that many fleets may not yet be fully equipped with sensors,” said Todd Sundsted, Chief Technology Officer (CTO) at Nautilus Labs.
“Being able to produce more accurate simulations even for vessels without sensors brings us much closer to achieving fleet-wide optimisation and efficiency rooted in machine learning-based simulations.”
