New up-to-date detailed bathymetric data on the Laptev Sea sector of the continental slope of the Eurasian Basin are considered. The exceptional importance of this area for studying its geological structure and a wide range of natural processes is noted. Current information on the present state of bathymetric coverage of the Laptev Sea sector of the continental slope of the Arctic Ocean, based on the international IBCAO Version 5 and GEBCO 2025 models, is presented. The unevenness of bathymetric coverage and the absence from these international bathymetric models of the results of Russian bathymetric surveys carried out in 2010–2024 are noted. Information on the results of modern bathymetric surveys conducted in 2022 and 2024 aboard the oceanographic research vessel Yantar in the Laptev Sea sector of the continental slope of the Eurasian Basin is presented. It is noted that a detailed areal survey of the seafloor relief in the Laptev Sea sector of the continental slope of the Eurasian Basin was conducted for the first time, and that the use of modern hydrographic information technologies made it possible to fully reveal the potential inherent in the hydroacoustic information provided by modern multibeam echo sounders. It is emphasized that the application of hydrographic information technologies made it possible to identify new relief forms in this area associated with the development of previously unstudied geological processes. A detailed bathymetric model of the relief of the entire survey area is presented, as well as a detailed relief model of a seafloor area with mud-volcanic forms. Differences in the structure of the seafloor surface of the Gakkel Ridge zone and the continental slope to the west of it are recorded. Several generations of landslide bodies and active and inactive valley forms are identified. Attention is drawn to the complex combination of neotectonic and gravitational processes in the Sadko Valley, which are clearly traceable in the detailed bathymetric relief model. Recommendations are given for updating regulatory documents on conducting bathymetric surveys to support marine geological research and on requirements for the final processing of areal seafloor survey data.

The study addresses the assessment of berth occupancy at sea passenger terminals under different configurations of regional route networks and their changes caused by external factors. The relevance of the problem is associated with the need to generate large data sets that can support informed decisions on passenger berth utilization and the development of new ferry lines using information obtained from digital port twins and forecasts of different ferry and cruise route scenarios. Sea passenger ports and terminals in the Baltic Sea region were selected as the object of the study. The need for multi-scenario simulation of ferry traffic taking into account berth characteristics and navigation-period data is substantiated. A new digital transport model describing the interaction between a group of sea terminals and ferry lines was developed to optimize ferry vessel handling. A new method for routing regional ferry services based on graph theory is also proposed for selecting cruise and ferry route options. The study applies multi-scenario simulation methods considering different traffic intensities and infrastructure characteristics of passenger ports. The model is based on actual data on Baltic Sea passenger ports and ferry traffic intensity. Its effectiveness is evaluated for the introduction of new ferry services involving St. Petersburg, Helsinki, Tallinn, and Stockholm. The effect of a new ferry line on berth occupancy at these ports is demonstrated experimentally. The developed software module of the digital port model provides data for both operational decision-making and forecasting the development of sea passenger ports. The simulation tools make it possible to assess the operation of a system of sea passenger ports and determine berth occupancy depending on changes in the route network. Multi-scenario data on berth occupancy and route-network changes can subsequently be used in optimization experiments to support strategic decisions on the development of sea passenger ports.

The study examines the shipping market model as a highly dynamic system affected by numerous external factors. These include long-term factors such as general trends in the development of the global economy and global trade, technological changes in maritime transportation, and deep-seated cycles of transformation in market conditions and structure within international supply chains involving maritime transport, as well as short-term manifestations of financial and economic crises, geopolitical conflicts, and other situational disturbances that temporarily affect market dynamics. The combined effect of these mechanisms produces complex and poorly predictable system behavior, which in dynamical systems theory is interpreted as changes in its properties and states over time. It is emphasized that, in many cases, the complex behavior of large dynamical systems results from the combined influence of numerous relatively simple factors that can be identified, quantified, and analyzed. It is concluded that decomposing a dynamical system such as the shipping market into elementary components, followed by an analysis of the interactions between its individual components, provides a reliable methodological basis for comprehensive analysis and forecasting of its behavior. This is particularly relevant in the current geopolitical situation in the global maritime transportation market and, to the greatest extent, in Russia. The study attempts to develop an innovative tool intended to explain underlying internal mechanisms and improve the reliability of forecasting methods in this field of knowledge.

The relevance of the study is determined by the maritime industry's transition from manual route planning to semi-automated and integrated navigation systems in the context of autonomous shipping. The requirements of IMO Resolution A.893(21), SOLAS 1974, and STCW 1978 make thorough voyage planning mandatory; however, existing systems differ in functionality and have limitations that complicate the selection of an optimal solution. The study provides a comparative analysis of leading ship route planning systems, identifying their capabilities, weaknesses, and development trends. The methodology includes analysis of the regulatory framework, technical documentation, and scientific publications, as well as comparative assessment according to the following criteria: preliminary route planning, under-keel clearance monitoring, weather routing optimization, carbon intensity accounting, and digital twin support. The results show that Wärtsilä and OneOcean are preferable for coastal navigation because of their advanced chart support, whereas StormGeo and ABB are better suited to transoceanic voyages owing to their more accurate hydrodynamic models. Common limitations include “black-box” algorithms, delays in weather-data updates, high subscription costs, cybersecurity issues, and incomplete integration with electronic chart display and information systems. Key development trends include artificial intelligence, digital twins, compliance with environmental regulations, low-Earth-orbit satellite communications, and autonomous shipping. The practical significance of the study lies in substantiating recommendations for selecting a system according to vessel type and navigation area. Further research should focus on hybrid systems combining physical vessel models with adaptive machine-learning and fuzzy-logic methods, as well as on standardized data-exchange protocols between shore-based optimization centers and shipboard ECDIS.

The study addresses the problem of improving the reliability of maritime situational monitoring by reconciling data from the Automatic Identification System (AIS) and radar. The relevance of the study stems from the need to ensure a reliable navigational picture when data from heterogeneous sensor sources are incomplete or contradictory. Real navigational logs of a target vessel were analysed; independent vessel tracks were constructed from each system, and discrepancies in position, speed, and timestamps were calculated. The results show that the two systems have different update rates and exhibit a systematic spatial offset, which substantially complicates their direct joint use without preprocessing. A neural network model for adaptive navigation data fusion is proposed. The model performs intelligent filtering of incoming data and detects anomalies without fixed a priori averaging of the input signals. Its architecture comprises two independent processing branches, one for radar data and the other for AIS data, and an adaptive fusion block that dynamically redistributes confidence weights according to the current uncertainty of each source. A mechanism for detecting instability in navigation data was implemented by analysing reconstruction-error statistics using a variance-based uncertainty criterion. This enables real-time assessment of incoming measurement quality and adaptive adjustment of source weights. Experimental validation on real navigational data demonstrated the operability of the proposed adaptive fusion model and its ability to generate a consistent target-vessel trajectory by dynamically redistributing confidence weights between the sources. The results confirm the functionality of the proposed architecture and its applicability to navigation tasks.

The article presents a method for the initial identification of parameters of a simplified mathematical model of ship motion with active control devices and the subsequent adaptation of the model parameters to changing conditions. A model based on the IEC 62065 standard is used as the mathematical model of ship motion with active control devices. The need to automate model parameter identification is determined by the labour-intensive process of determining hydrodynamic and aerodynamic characteristics required for adequate simulation of ship motion, as well as by the fact that a ship is a dynamic object whose motion characteristics may change depending on heel, trim, hull fouling, and other factors. The article briefly discusses existing problems associated with developing mathematical models of ship motion and identifying their parameters. The operating principle of the genetic algorithm is explained to provide an understanding of the approach without distracting from the main objective of the study. Two standard manoeuvres used to determine ship manoeuvring characteristics, the Turning Circle and Zig-Zag manoeuvres, were selected for the experiments. In the Turning Circle experiment, parameters obtained from the ship's technical documentation based on sea trials were compared with simulation results obtained after manual parameter tuning and parameter identification using a genetic algorithm. The experiment was aimed at identifying the model parameters, adjusting the genetic algorithm settings to the task, and improving simulation accuracy. In the Zig-Zag experiment, a change in the parameters of the mathematical model of ship motion was simulated at a specified point in time. An Observer developed on the basis of the mathematical model was used to provide reference dynamic parameters of the ship; in actual operation, it represents a set of onboard measuring systems. The genetic algorithm is intended to retune the model so as to minimize the difference between the dynamic parameters of ship motion and those calculated by the mathematical model.

The article examines risk assessment methods for the operation of an unmanned port tug in port waters. The relevance of the study is determined by the increasing tonnage and dimensions of serviced vessels and the active introduction of autonomous shipping technologies, including MASS at the second level of autonomy according to the IMO classification. At this level, the system independently performs basic navigation and manoeuvring tasks, while a shore-based remote operator monitors its operation and intervenes only in abnormal situations. The transition to unmanned operation substantially changes the structure of operational risks: risks to people on board are reduced, whereas the significance of autonomous system failures, loss of communication, cyber threats, and remote-operator errors increases under conditions of intensive port traffic. Based on an analysis of Russian and international scientific literature, modern risk assessment methods, including HAZOP/CHAZOP, SWIFT, HRA, PHA, and scenario analysis, are reviewed. Their advantages and limitations for highly automated and remotely controlled technical systems are identified. A combined approach integrating Preliminary Hazard Analysis, the Structured What-If Technique, Human Reliability Assessment, and expert assessment is substantiated. A comprehensive risk assessment map comprising 12 key groups of hazards is developed. Critical risks associated with severe weather conditions and cyber threats are identified, their probability and consequence severity are assessed on a five-point scale, and organizational and technical risk-control measures are proposed. The results provide a methodological basis for developing a risk inventory, a safety management system, operational documentation, and further regulatory provisions for the safe operation of unmanned port tugs.

A method for the experimental determination of the amplitude-frequency characteristic of ship rolling directly during an operational voyage is presented, taking into account the actual voyage loading condition, vessel condition, and actual hydrometeorological environment. The methodological basis comprises three successive stages: instrumental recording and spectral processing of the roll record with correction for the mean wind-induced heel; obtaining the total wave spectrum by combining data obtained from radar image processing with low-frequency swell detected through the frequency invariant of rolling and associated with remote cyclonic systems, information on which can be obtained from synoptic charts; and calculation of the amplitude-frequency characteristic from the ratio of the roll and wave spectra, followed by generalization of partial characteristics for different heading angles. The method was tested using voyage data from a vessel of the Sokol design off the coast of Kamchatka under significant wave conditions with several wave systems, including detected swell from a tropical cyclone. The partial amplitude-frequency characteristics obtained for three heading angles show invariance in the frequency positions of their maxima despite substantial differences in their amplitude values, indicating the high sensitivity of the method to the accuracy of approximation procedures. The proposed procedure for arithmetic averaging of the amplitude-frequency characteristic over a set of heading angles proved valid in verification roll prediction: the frequency composition is reproduced adequately, while the differences between predicted and actual amplitudes do not exceed three degrees and are considerably smaller than the scatter of the maxima of the partial amplitudefrequency characteristics. Alternative generalization methods, including heading-specific extrema and weightedaverage estimates, are discussed, and reducing the dependence of the results on spectral approximation parameters is identified as a priority for further improvement of the method.

The objective of the present study is to analyze changes in the dimensions of dunes and channel-bed waves under natural conditions and under design conditions with a dredge cut, with the aim of developing a method for accounting for them when determining depth reduction. An additional objective is to determine the steepness of dunes and waves depending on their location on the side bar. The study is based on experimental data obtained in a hydraulic flume with a movable sandy bed composed of side bars. The results show that, as the flow depth decreases from the trough to the crest of the side bars, the relative heights of dunes (the ratio of dune height to flow depth) increase from 0.08 to 0.18. Beneath the dunes, second-level channel-bed waves are formed, whose lengths are three to four times greater than the dune length, while their heights are close to the dune height. At the same time, the relative wave heights vary in proportion to changes in dune heights. A riffle reach was subjected to detailed analysis in its natural state and in the design state with a dredge cut. In the natural state, the relative dune height was 0.13, whereas in the dredge cut it was 0.07. The relative heights of second-level waves also decreased from 0.15 to 0.09. Such features of the bed relief in dredge cuts must be taken into account when designing dredging operations. A method is proposed for calculating depth reduction in the navigation channel within dredge cuts based on the combined effect of dunes and second-level channel-bed waves. In addition, the steepness of dunes and waves was analyzed at different locations on the side bars. It was established that there is no pronounced dependence of the steepness of these forms on flow depth or location.

The article presents a classification and retrospective analysis of existing methods for accounting for greenhouse gas and pollutant emissions from seagoing ships. The main stages of model development are considered, from early top-down and bottom-up approaches based on aggregated fuel sales statistics and average ship characteristics to modern high-precision systems using shore-based and satellite Automatic Identification System (AIS) data, Long-Range Identification and Tracking (LRIT) data, as well as Monitoring, Reporting and Verification (MRV) data. Regional and global models (HELCOM, OSPAR, MEET, EMEP, EDGAR, MEERI, STEAM 1.0, 2.0, 3.0), their advantages and limitations, as well as hybrid approaches and chemical transport models are described in detail. Key scientific tasks for further development of the methodologies are identified, including accounting for hydrodynamic resistance, ice conditions, trim, biofouling, alternative power plants, and ship digital twins. Based on the analysis, initial technical requirements for the development of a hybrid model for assessing greenhouse gas emissions for the Russian Federation are substantiated. The model integrates satellite AIS, LRIT, national ship databases, fuel statistics, and hydrodynamic calculations adapted to Arctic and inland waterways, which will support both international reporting and domestic environmental monitoring.

The paper presents a systematic analysis of the current state of research on the application of reinforcement learning methods to optimize production logistics and supply chain management in the shipbuilding industry. The specific features of shipbuilding as engineer-to-order production are described in detail; in this type of production, each product is designed and manufactured from scratch in accordance with unique customer requirements, which distinguishes it from serial production. It is shown that this feature places increased demands on the adaptability of planning systems, which must operate under conditions of uncertainty and frequent changes. A comparative analysis is conducted of three main classes of optimization methods: classical heuristic dispatching rules, metaheuristic approaches, including genetic algorithms and swarm intelligence algorithms, and various types of deep reinforcement learning algorithms. The methods are compared according to important criteria of solution quality, computational efficiency, robustness to disturbances, and the ability to continue learning in real time. It is noted that reinforcement learning provides greater adaptability and faster decision making than traditional methods, especially in a dynamically changing production environment. Special attention is paid to digital twins as an essential environment for the safe training of agents in accordance with the requirements of the international standard; it is shown that only such an environment makes it possible to conduct repeated experiments without interrupting actual production. Examples of the implementation of reinforcement learning elements at shipyards and in related industries, including pilot projects in South Korea, Spain, and Norway, are systematized. Based on the identified gaps, promising research directions are formulated: the development of multi-agent systems for coordinating multiple decisions, the use of transfer learning for accelerated adaptation to new order types, and integration with generative artificial intelligence for synthesizing alternative planning scenarios.

The paper examines the problem of minimizing or completely eliminating the negative impact of vessels on the biological resources of the northern seas in the context of the growing importance of Russia’s Northern Sea Route and the planned increase in cargo transportation. It is noted that ship power plant cooling systems consume significant amounts of water from the surrounding sea area. It is shown that plankton, fish eggs, and juvenile fish entering the system are almost completely killed by mechanical and thermal effects. Particular attention is drawn to the fact that this destroys the basis of the food chain, which inevitably leads to the depletion of the biological resources of the northern seas and a decrease in fish productivity. It is concluded that methods for minimizing and, where possible, completely eliminating this negative impact need to be identified and developed. The study identifies possible solutions to this problem, including temperature control of the system, the use of pumps with variable-frequency drives, and a transition to fully closed-loop cooling systems. The limitations, advantages, and disadvantages of each of these approaches are identified. The northern seas, characterized by low water temperatures, provide additional opportunities for implementing the proposed approaches. It is also taken into account that the power plant is often operated at partial loads, which reduces the heat rejection required from the cooling system. Using the existing regulatory methodology for calculating damage caused to aquatic biological resources and the integral performance indicator for ship power plant cooling systems proposed in the study, which takes into account technical and environmental indicators, the considered cooling-system options are compared for the conditions of different seas.

The subject of the study is the development of a dynamic mathematical model of the reliability of the ergatic system “navigator–information system” and an algorithm for adaptive reconfiguration of the human–machine interface. Existing approaches to accounting for the human factor in ship navigation are considered, including models of exponential decline in vigilance, two-level structures of ergatic systems, and static scales for assessing the complexity of the navigational situation. It has been established that a comprehensive model integrating these factors with an adaptive interface is currently lacking. As a result, the navigator’s vigilance coefficient has been formalized by introducing a cognitive-load multiplier; a probabilistic assessment of operator errors depending on the navigational situation has been proposed; and an adaptive control algorithm has been developed that reduces the increase in erroneous actions during a watch. The aim of the study is to develop a dynamic mathematical model of the reliability of the specified ergatic system and an algorithm for adaptive reconfiguration of the human–machine interface. To achieve this aim, the following tasks have been accomplished: the navigator’s vigilance coefficient has been formalized by introducing a cognitive-load multiplier; a probabilistic assessment of operator errors depending on the complexity of the navigational situation has been proposed; an adaptive control algorithm has been developed that reduces the increase in erroneous actions during a watch; and simulation modeling using the Monte Carlo method has been carried out for three typical ship-navigation scenarios. The scientific novelty lies in the development of a dynamic model with feedback that ensures adaptation of the interface to the operator’s current state. The practical significance of the results lies in the possibility of applying the developed model in the design of decision support systems on the ship’s bridge and in simulator training for navigators.

The subject of the study is the development of an intelligent decision support system based on fuzzy logic for marine diesel engines, aimed at improving diagnostic accuracy and operational reliability under conditions of incomplete, uncertain, or fragmented data. The relevance of the study is determined by the increasing complexity of parameters monitored by shipboard automation systems and the stringent requirements for safe and efficient engine operation, which create significant difficulties for shipboard specialists in real-time information analysis. The study examines the application of fuzzy logic, production rules, intelligent data analysis methods, and expert knowledge bases to assess the technical condition of marine diesel engines, detect faults at the individual-cylinder level, determine permissible operating modes with due regard to technical and navigational conditions, predict gradually developing faults, and prioritize maintenance activities. The architecture of an intelligent decision support system is developed to provide a transition from conventional monitoring to intelligent information-oriented control in human–machine systems. A fault and troubleshooting database and a knowledge base formalizing production rules using the concept of deviations are considered. A fuzzy inference procedure is implemented, including fuzzification of input parameters obtained from engine monitoring systems, processing using the Mamdani–Zadeh method, and defuzzification of the results to obtain real-time assessments of engine condition. The system provides a graphical interface displaying parameter deviations, probable faults, and operational recommendations. The results show that the proposed approach effectively approximates expert reasoning, accounts for nonlinearities and uncertainties, and provides a basis for improving the efficiency of predictive maintenance, optimizing operating modes, and improving the overall operating efficiency of marine diesel engines.