The human element in data driven decision making for winter navigation

Abstract

With the availability of big maritime data and advancements of the computational techniques, such as machine learning and AI, automation of navigational decision-making in ships is on the rise. For low risk and more frequently observed cases, such as local vessels operating in calm sea, abundant data facilitates straightforward automation. The traditional data driven modeling (including black-box models) and associated validation techniques suffice the automation process of these cases as human intervention is rarely needed. However, for high-risk and infrequent scenarios, like winter navigation, data may be scarce, sparse, or imbalanced. Black-box data-driven models and associated validation techniques prove insufficient in these cases, as the expectation is for human to jump in and take control over when needed. This paper explores the role of the human element in various stages of data driven decision-making for winter navigation, encompassing the establishment of a multipurpose winter navigation database, model development, and validation. To illustrate, a case study on ice-breaker assistance operations will be presented.

Keywords: winter navigation, marine automation, data driven decision-making

DOI: 10.54941/ahfe1005261