To meet the increasing sailing demand of the Northeast Passage of the Arctic, a daily prediction model of sea ice concentration (SIC) based on the convolutional long short-term memory network (ConvLSTM) algorithm was proposed in this study. Previously, similar deep learning algorithms (such as convolutional neural networks; CNNs) were frequently used to predict monthly changes in sea ice. To verify the validity of the model, the ConvLSTM and CNNs models were compared based on their spatiotemporal scale by calculating the spatial structure similarity, root-mean-square-error, and correlation coefficient. The results show that in the entire test set, the single prediction effect of ConvLSTM was better than that of CNNs. Taking 15 December 2018 as an example, ConvLSTM was superior to CNNs in simulating the local variations in the sea ice concentration in the Northeast Passage, particularly in the vicinity of the East Siberian Sea. Finally, the predictability of ConvLSTM and CNNs was analysed following the iteration prediction method, demonstrating that the predictability of ConvLSTM was better than that of CNNs.

This paper presents a new route planning system for the purpose of evaluating the strategic prospects for future Arctic routes. The route planning problem can be regarded as a multi criteria decision making problem with large uncertainties originating from multi-climate models and experts’ knowledge and can be solved by a modified A* algorithm where the hesitant fuzzy set theory is incorporated. Compared to the traditional A* algorithm, the navigability of the Arctic route is firstly analyzed as a measure to determine the obstacle nodes and three key factors to the vessel navigation including sailing time, economic cost and risk are overall considered in the HFS-A* algorithm. A numerical experiment is presented to test the performance of the proposed algorithm.

With the accelerated melting of the Arctic sea ice, the opening of the Northeast Passage of the Arctic is becoming increasingly accessible. Nevertheless, the constantly changing natural environment of the Arctic and its multiple impacts on vessel navigation performance have resulted in a lack of confidence in the outcomes of polar automated route planning. This paper aims to evaluate the effectiveness of two distinct models by examining the advancements in two essential components of e-navigation, namely ship performance methods and ice routing algorithms. We also seek to provide an outlook on the future directions of model development. Furthermore, through comparative experiments, we have examined the existing research on ice path planning and pointed out promising research directions in future Arctic Weather Routing research.

With the accelerated melting of the Arctic sea ice, the opening of the Northern Sea Route (NSR) of the Arctic is becoming increasingly accessible. The purpose of this paper was to examine the impact of uncertainty in sea ice thickness (SIT) data on the opening of the NSR and to quantify the extent of this impact, which is essential to the regularized operation of polar shipping. A quantitative assessment framework was proposed to examine the influence of SIT data uncertainty on navigational uncertainty through three aspects: the navigational spatiotemporal windows, the distribution of safe sailing speeds, and the uncertainty in optimal route planning by employing four distinct SIT datasets. Furthermore, the sensitivity of navigational spatiotemporal windows, the distribution of safe sailing speeds, and route planning to variations in SIT were also evaluated. Results show that experiment results based on CS2SMOS exhibit a more aggressive profile, while results based on CPOM are more conservative. The difference in SIT data has a significant impact on the proportion of operations subject to special consideration areas, such as safety speed, sailing time, and distance in NEP. A 0.1 m discrepancy in sea ice thickness data results in an approximate 0.067 change in the proportion of operations within special consideration areas. This discrepancy also leads to an approximate speed change of 0.89 knots, a navigation duration change of approximately 4 days, and a distance change of 152 km within specified limits.

The reconstruction and monitoring of visibility over marine environments is critically important because of a lack of observations. To travel safely in marine environments, a high quality of visibility data is needed to evaluate navigation risk. Currently, although visibility is available through numerical weather prediction models as well as ground and spaceborne remote sensing platforms and ship measurements, issues still exist over the remote marine environments and northern latitudes. To improve visibility prediction and reduce navigational risks, gridded visibility data based on artificial neural network analysis can be used over marine environments, and the problem can be regarded as an air quality prediction problem based on machine learning algorithms. This new method based on artificial intelligence techniques developed here is tested over the Indian Ocean. The mean error of the inferred visibility from the artificial neural network analysis is found to be less than 8.0%. The results suggested that satellite-based optical thickness and numerical model-based reanalysis data can be used to infer gridded visibility values based on artificial neural network analysis, and that could help us reconstruct and monitor surface gridded visibility values over marine and remote environments.