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Big Data Analytics (BDA) provides valuable opportunities for the optimization of maritime shipping management and operations. This might have a significant and beneficial impact on the Chinese maritime industry, which has recently emerged as a prominent player on the global stage due to the fast development of its maritime infrastructures and economical opportunities. This paper introduces two-field research conducted by a web-based questionnaire survey and semi-structured interviews with a large number of stakeholders in the maritime sector. The analyses show the impact of the development of big data technologies as well as current obstacles which constrain their deployment in the global maritime sector. The paper finally suggests several directions for promoting the wide-scale utilization of BDA in the maritime industry.

The accuracy and interpretability of ship energy consumption prediction results are important for ship energy efficiency optimization. In order to improve the accuracy of ship energy consumption prediction and enhance the model interpretability, this paper proposes a ship energy consumption prediction method based on Stacking and SHAP. Firstly, based on Stacking theory, multiple heterogeneous and complementary base models were selected using residual correlation analysis methods to construct a fusion model. And then, to address the “black box” characteristics of the fusion model, SHAP is used to analyze the base model and energy consumption impact characteristics of the fusion model in terms of their interpretability. A large container ship is used as the research object to verify the effectiveness and interpretability of the proposed method. The experimental results show that, in terms of accuracy, compared with the best single model (RF), the mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) of the Stacking fusion model are reduced by 4.1%, 16.1%, and 8.3%, respectively, and the R² is improved by 1.5%. Meanwhile, in terms of interpretability, SHAP reveals that Random Forest (RF), k-Nearest Neighbor (KNN), and Gradient Boosting (GB) models play a dominant role in the fusion model, with a total contribution value of about 67%. In addition, sailing speed, mean draft, and trim are the main factors affecting the energy consumption of a ship, and the contribution value of each influential feature can be quantitatively measured. The proposed method ensures the prediction accuracy while enhancing the model interpretability, which can provide more reliable and transparent decision support for ship energy efficiency management.

In recent years, the maritime domain has experienced tremendous growth due to the exploitation of big traffic data. Particular emphasis has been placed on deep learning methodologies for decision-making. Accurate Vessel Traffic Flow Forecasting (VTFF) is essential for optimizing navigation efficiency and proactively managing maritime operations. In this work, we present a distributed Unified Approach for VTFF (dUA-VTFF), which employs Transformer models and leverages the Apache Spark big data distributed processing framework to learn from historical maritime data and predict future traffic flows over a time horizon of up to 30 min. Particularly, dUA-VTFF leverages vessel timestamped locations along with future vessel locations produced by a Vessel Route Forecasting model. These data are arranged into a spatiotemporal grid to formulate the traffic flows. Subsequently, through the Apache Spark, each grid cell is allocated to a computing node, where appropriately designed Transformer-based models forecast traffic flows in a distributed framework. Experimental evaluations conducted on real Automatic Identification System (AIS) datasets demonstrate the improved efficiency of the dUA-VTFF compared to state-of-the-art traffic flow forecasting methods.

The shipping company or the operator determines the mode of operation of a ship. In the case of container ships, there may be various operating patterns employed to arrive at the destination within the stipulated time. In addition, depending on the influence of the ocean’s environmental conditions, the speed and the route can be changed. As the ship’s fuel oil consumption is closely related to its operational pattern, it is possible to identify the most economical operations by analyzing the operational patterns of the ships. The operational records of each shipping company are not usually disclosed, so it is necessary to estimate the operational characteristics from publicly available data such as the automatic identification system (AIS) data and ocean environment data. In this study, we developed a visualization program to analyze the AIS data and ocean environmental conditions together and propose two categories of applications for the operational analysis of container ships using maritime big data. The first category applications are the past operation analysis by tracking previous trajectories, and the second category applications are the speed pattern analysis by shipping companies and shipyards under harsh environmental conditions. Thus, the operational characteristics of container ships were evaluated using maritime big data.

In this paper, an innovative framework labeled as cooperative cognitive maritime big data systems (CCMBDSs) on the sea is developed to provide opportunistic channel access and secure communication. A two-phase frame structure is applied to let Secondary users (SUs) entirely utilize the transmission opportunities for a portion of time as the reward by cooperation with Primary users (PUs). Amplify-and-forward (AF) relaying mode is exploited in SU nodes, and Backward induction method based Stackelberg game is employed to achieve optimal determination of SU, power consumption and time portion of cooperation both for non-secure communication scenario and secure communication. Specifically, a jammer-based secure communications scheme is developed to maximize the secure utility of PU, to confront of the situation that the eavesdropper could overheard the signals from SUi and the jammer. Close-form solutions for the best access time portion as well as the power for SUi and jammer are derived to realize the Nash Equilibrium. Simulation results validate the effectiveness of our proposed strategy.

ObjectiveDuring the last few years, underwater object detection and marine resource utilization have gained significant attention from researchers and become active research hotspots in underwater image processing and analysis domains. This research study presents a data fusion-based method for underwater salient object detection and ocean environment monitoring by utilizing a deep model.MethodologyA hybrid model consists of an upgraded AlexNet with Inception v-4 for salient object detection and ocean environment monitoring. For the categorization of spatial data, AlexNet is utilized, whereas Inception V-4 is employed for temporal data (environment monitoring). Moreover, we used preprocessing techniques before the classification task for underwater image enhancement, segmentation, noise and fog removal, restoration, and color constancy.ConclusionThe Real-Time Underwater Image Enhancement (RUIE) dataset and the Marine Underwater Environment Database (MUED) dataset are used in this research project’s data fusion and experimental activities, respectively. Root mean square error (RMSE), computing usage, and accuracy are used to construct the model’s simulation results. The suggested model’s relevance form optimization and conspicuous item prediction issues in the seas is illustrated by the greatest accuracy of 95.7% and low RMSE value of 49 when compared to other baseline models.

Marine ecosystems are in a state of crisis worldwide due to anthropogenic stressors, exacerbated by generally diminished ocean literacy. In other sectors, big data and technological advances are opening our horizons towards improved knowledge and understanding. In the marine environment the opportunities afforded by big data and new technologies are limited by a lack of available empirical data on habitats, species, and their ecology. This limits our ability to manage these systems due to poor understanding of the processes driving loss and recovery. For improved chances of achieving sustainable marine systems, detailed local data is required that can be connected regionally and globally. Citizen Science (CS) is a potential tool for monitoring and conserving marine ecosystems, particularly in the case of shallow nearshore habitats, however, limited understanding exists as to the effectiveness of CS programmes in engaging the general public or their capacity to collect marine big data. This study aims to understand and identify pathways for improved engagement of citizen scientists. We investigated the motivations and barriers to engagement of participants in CS using two major global seagrass CS programmes. Programme participants were primarily researchers in seagrass science or similar fields which speak to a more general problem of exclusivity across CS. Altruistic motivations were demonstrated, whilst deterrence was associated with poor project organisation and a lack of awareness of specified systems and associated CS projects. Knowledge of seagrass ecosystems from existing participants was high and gains because of participation consequently minimal. For marine CS projects to support big data, we need to expand and diversify their current user base. We suggest enhanced outreach to stakeholders using cooperatively identified ecological questions, for example situated within the context of maintaining local ecosystem services. Dissemination of information should be completed with a variety of media types and should stress the potential for knowledge transfer, novel social interactions, and stewardship of local environments. Although our research confirms the potential for CS to foster enhanced collection of big data for improved marine conservation and management, we illustrate the need to improve and expand approaches to user engagement to reach required data targets.

Ocean observation plays an essential role in ocean exploration. Ocean science is entering into big data era with the exponentially growth of information technology and advances in ocean observatories. Ocean observatories are collections of platforms capable of carrying sensors to sample the ocean over appropriate spatiotemporal scales. Data collected by these platforms help answer a range of fundamental and applied research questions. Many countries are spending considerable amount of resources on ocean observing programs for various purposes. Given the huge volume, diverse types, sustained measurement, and potential uses of ocean observing data, it is a typical kind of big data, namely marine big data. The traditional data-centric infrastructure is insufficient to deal with new challenges arising in ocean science. New distributed, large-scale modern infrastructure backbone is urgently required. This paper discusses some possible strategies to solve marine big data challenges in the phases of data storage, data computing, and analysis. Some applications in physics, chemistry, geology, and biology illustrate the significant uses of marine big data. Finally, we highlight some challenges and key issues in marine big data.

Ocean fronts, widespread across the global ocean, cause abrupt shifts in physical properties such as temperature, salinity, and sound speed, significantly affecting underwater acoustic communication and detection. While past research has concentrated on qualitative analysis and small-scale research on ocean front sections, a comprehensive analysis of ocean fronts’ characteristics and their impact on underwater acoustics is lacking. This study employs high-resolution reanalysis data and in situ observations to accurately identify ocean fronts, sound speed structures, and acoustic propagation features from over six hundred thousand Kuroshio Extension Front (KEF) sections. Utilizing marine big data statistics and machine learning evaluation metrics such as out-of-bag (OOB) error and Shapley values, this study quantitatively assesses the variations in sound speed structures across the KEF and their effects on acoustic propagation shifts. This study’s key findings reveal that differences in sound speed structure are significantly correlated with KEF strength, with the channel axis depth and conjugate depth increasing with front strength, while the thermocline intensity and depth excess decrease. Acoustic propagation features in the KEF environment exhibit notable seasonal variations.

Cui, J., 2018. Dynamic migration algorithm of marine big data in cloud computing environment. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 706–712. Coconut Creek (Florida), ISSN 0749-0208. Aiming at the shortcomings of the current migration algorithm based on the importance of data, like poor load balancing and large consumption of resources, the paper proposes a dynamic migration algorithm for marine big data in cloud computing environment. Firstly, the characteristics of marine big data are analyzed. And then the Moran's I index is used to analyze the correlation index, and the storage of marine big data in cloud computing environment is positioned. Finally, adaptive inertia weight is introduced based on the firefly algorithm, through the objective function of the bandwidth to calculate the minimum value of the bandwidth and use as the optimal solution, and the dynamic migration of marine big data is realized. Experiments show that the proposed algorithm not only has better migration performance, but also takes into account migration costs and load balancing, resource utilization and bandwidth utilization.