Management of environmental streaming data to optimize Arctic shipping routes.

Abstract

Dynamic accurate predictions of Arctic sea ice, ocean, atmosphere, and ecosystem are necessary for safe and efficient Arctic maritime transportation; however a related technical roadmap has not yet been established. In this paper, we propose a management system for trans-Arctic maritime transportation supported by near real-time streaming data from air-space-ground-sea integrated monitoring networks and high spatio-temporal sea ice modeling. As the core algorithm of integrated monitoring networks, a long short-term memory (LSTM) neural network is embedded to improve Arctic sea ice mapping algorithms.Since the LSTM is localized in time and space, it can make full use of streaming data characteristics. The sea ice–related parameters from satellite remote sensing raw data are used as the input of the LSTM, while streaming data from shipborne radar networks and/or buoy measurements are used as training datasets to enhance the accuracy and resolution of environmental streaming data from outputs of LSTM. Due to large size of streaming data, the proposed management system of trans-Arctic shipping should be built on a cloud distribution platform using existing wireless communications networks among vessels and ports. Our management system will be used by the ongoing European Commission Horizon 2020 Programme “ePIcenter.”