Search Results (14)

This study addresses the critical challenge of lacking quantitative measures for objective evaluation of vessel traffic service (VTS) operator workload, where current uniform staffing approaches fail to reflect center-specific operational characteristics. The VTS Operator Workload Index (VOWI) model was developed using the Delphi–AHP methodology to determine the relative importance of key factors including traffic, sea area characteristics, port facilities, and weather conditions, which formed the basis for calculating both center-wide and per-operator workload indices. Factor analysis revealed that traffic factors showed the highest importance at 0.4627, followed by sea area (0.1960), port facilities (0.1916), and weather (0.1497) factors. Application of the VOWI model to 19 VTS centers in South Korea demonstrated that per-operator workload at Busan, Incheon, and Ulsan VTS was up to three times higher than at other centers. This finding indicates that the current uniform staffing approach based on sector count inadequately reflects each center’s actual operational characteristics. The VOWI model provides objective criteria for efficient personnel management in VTS centers and is expected to contribute to improving VTS service quality. Full article

This study is aimed to improve the intelligence level, efficiency, and accuracy of ship safety and security systems by contributing to the development of marine weather forecasting. The accurate and prompt recognition of weather fax charts is very important for navigation safety. This study employed many artificial intelligent (AI) methods including a vectorization approach and target recognition algorithm to automatically detect the severe weather information from Japanese and US weather charts. This enabled the expansion of an existing auto-response marine forecasting system’s applications toward north Pacific and Atlantic Oceans, thus enhancing decision-making capabilities and response measures for sailing ships at actual sea. The OpenCV image processing method and YOLOv5s/YOLO8vn algorithm were utilized to make template matches and locate warning symbols and weather reports from surface weather charts. After these improvements, the average accuracy of the model significantly increased from 0.920 to 0.928, and the detection rate of a single image reached a maximum of 1.2 ms. Additionally, OCR technology was applied to retract texts from weather reports and highlighted the marine areas where dense fog and great wind conditions are likely to occur. Finally, the field tests confirmed that this auto and intelligent system could assist the navigator within 2–3 min and thus greatly enhance the navigation safety in specific areas in the sailing routes with minor text-based communication costs. Full article

This paper analyses the application of deep learning techniques for predicting wave overtopping events in port environments using sea state and weather forecasts as inputs. The study was conducted in the outer port of Punta Langosteira, A Coruña, Spain. A video-recording infrastructure was installed to monitor overtopping events from 2015 to 2022, identifying 3709 overtopping events. The data collected were merged with actual and predicted data for the sea state and weather conditions during the overtopping events, creating three datasets. We used these datasets to create several machine learning models to predict whether an overtopping event would occur based on sea state and weather conditions. The final models achieved a high accuracy level during the training and testing stages: 0.81, 0.73, and 0.84 average accuracy during training and 0.67, 0.48, and 0.86 average accuracy during testing, respectively. The results of this study have significant implications for port safety and efficiency, as wave overtopping events can cause disruptions and potential damage. Using deep learning techniques for overtopping prediction can help port managers take preventative measures and optimize operations, ultimately improving safety and helping to minimize the economic impact that overtopping events have on the port’s activities. Full article

Typically, images captured in adverse weather conditions such as haze or smog exhibit light gray or white color on screen; therefore, existing hazy image restoration studies have performed dehazing under the same assumption. However, hazy images captured under actual weather conditions tend to change color because of various environmental factors such as dust, chemical substances, sea, and lighting. Color-shifted hazy images have hindered accurate color perception of the images, and due to the dark haze color, they have worsened visibility compared to conventional hazy images. Therefore, various color correction-based dehazing algorithms have recently been implemented to restore colorcast images. However, existing color restoration studies are limited in that they struggle to distinguish between haze and objects, particularly when haze veils and images have a similar color or when objects with a high saturation value occupy a significant portion of the scene, resulting in overly grayish images and distorted colors. Therefore, we propose a saturation-based dehazing method that extracts only the hue of the cast airlight and preserves the information of the object. First, the proposed color correction method uses a dominant color extraction method for the clustering of CIELAB(LAB) color images and then assigns area scores to the classified clusters. Sorting of the airlight areas is performed using the area score, and gray world-based white balance is performed by extracting the hue of the area. Finally, the saturation of the restored image is used to separate and process the distant objects and airlight, and dehazing is performed by applying a weighting value to the depth map based on the average luminance. Our color restoration method prevents excessive gray tone and color distortion. In particular, the proposed dehazing method improves upon existing issues where near-field information is lost and noise is introduced in the far field as visibility improves. Full article

The power load data of electric-powered ships vary with the ships’ operational status and external environmental factors such as sea conditions. Therefore, a model is required to accurately predict a ship’s power load, which depends on changes in the marine environment, weather environment, and the ship’s situation. This study used the power data of an actual ship to predict the power load of the ship. The research on forecasting a ship’s power load fluctuations has been quite limited, and the existing models have inherent limitations in predicting these fluctuations accurately. In this paper, A multiple feature extraction (MFE)-long short-term memory (LSTM) model with skip connections is introduced to address the limitations of existing deep learning models. This novel approach enables the analysis and forecasting of the intricate load variations in ships, thereby facilitating the prediction of complex load fluctuations. The performance of the model was compared with that of a previous convolutional neural network-LSTM network with a squeeze and excitation (SE) model and deep feed-forward (DFF) model. The metrics used for comparison were the mean absolute error, root mean squared error, mean absolute percentage error, and R-squared, wherein the best, average, and worst performances were evaluated for both models. The proposed model exhibited a superior predictive performance for the ship’s power load compared to that of existing models, as evidenced by the performance metrics: mean absolute error (MAE) of 55.52, root mean squared error of (RMSE) 125.62, mean absolute percentage error (MAPE) of 3.56, and R-squared (R²) of 0.86. Therefore, the proposed model is expected to be used for power load prediction during electric-powered ship operations. Full article

Sea surface temperature (SST), a critical parameter of the global ocean–atmosphere system, is an essential element in the study and in the application of marine science. Satellite–infrared observations currently represent the only available method for continuous, large-scale observation of SST. Although passive microwave observations are not blocked by clouds, allowing for data collection in all weather conditions, this technological tool is characterized by low spatial resolution. Conversely, infrared observations offer high resolution but are susceptible to cloud obscuration. Accordingly, a technique that effectively fuses microwave and infrared satellite observations into a high-resolution SST field with global coverage close to the actual distribution is of practical significance. This paper describes fusing MODIS infrared remote sensing and AMSR-2 microwave remote sensing SST data with an optimal interpolation (OI) approach to produce a high-resolution SST data. The study chose the coastal Kuroshio region of China to establish an appropriate scale for examining the spatial structure of SST and attaining a more realistic picture of SST observations and impacts. The included discussion of the sources of error in the fusion process provides a reference for improving the accuracy of fused marine remote sensing data. The study also compared the fused SST results and the current international mainstream multi-temporal resolution of the three using the OI algorithm. We compared the fusion product with ARGO data with and without typhoon impact to explore and practice the OI in SST fusion when evaluating the accuracy of different data in the case of external disturbance being present. The research results have great significance for improving regional SST forecast accuracy while ensuring the applicability of various approaches to fusing SST data by incorporating the influence of typhoons in the offshore region of the East China Sea (ECS). Implications for the future development of SST fusion data are also included in the discussion. Full article

Compared to highway road tunnels, the entrance section of cross-river and cross-sea tunnels feature long and steep slopes. Along with a complicated traffic environment and harmful weather conditions, traffic congestion and rear-end crashes occur frequently during car-following in cross-river and cross-sea tunnels. It is necessary to examine the impact of traffic flow and weather conditions on car-following behavior at the entrance section of cross-river and cross-sea tunnels. To this end, this paper first extracted the vehicle speed data based surveillance video at the entrance of the Shanghai Yangtze River Tunnel. Moreover, the actual average speed under different traffic flow conditions was obtained through the clustering algorithm, which was used as the basis for setting the experimental parameters. Then, in the driving simulation experiment, three traffic flow conditions (free flow, congested flow, and jam flow) were set up in three weather conditions (sunny, rainy, and snowy), and a risk situation was set up in each condition. Distance headway, time headway, acceleration, lateral offset, and driver’s emergency response time were collected. Moreover, seven slopes of 2% to 5% were set, and the relationship of slope on longitudinal speed and lateral offset was analyzed. ANOVA and post-hoc analyses were applied. The result indicates that traffic flow conditions have a significant effect on the car-following behavior, while weather conditions mainly influence the time headway. Moreover, drivers tend to adopt more cautious driving behavior as the distance between the vehicle and the tunnel entrance decreases. The results also show that the slope of the cross-river and cross-sea tunnel entrance section has a major influence on vehicle speed. Full article

Changes in the seafloor relief are particularly noticeable in shallow waterbodies (at depths up to several metres), where they are of significance for human safety and environmental protection, as well as for which the highest measurement accuracy is required. The aim of this publication is to present the integration data model of the bathymetric monitoring system for shallow waterbodies using Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicles (USV). As part of this model, three technology components will be created: a hydroacoustic and optoelectronic data integration component proposed by Dąbrowski et al., a radiometric depth determination component based on optoelectronic data using the Support Vector Regression (SVR) method, and a coastline extraction component proposed by Xu et al. Thanks to them, it will be possible to cover the entire area with measurements in the coastal zone, in particular between the shallow waterbody coastline and the min. isobath recorded by the echo sounder (the area is lacking actual measurement data). Multisensor data fusion obtained using Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS), Light Detection And Ranging (LiDAR), Real Time Kinematic (RTK), UAV, and USV will allow to meet the requirements provided for the International Hydrographic Organization (IHO) Special Order (horizontal position error ≤ 2 m (p = 0.95), vertical position error ≤ 0.25 m (p = 0.95)). To this end, bathymetric and photogrammetric measurements shall be carried out under appropriate conditions. The water transparency in the tested waterbody should be at least 2 m. Hydrographic surveys shall be performed in windless weather and the water level is 0 in the Douglas sea scale (no waves or sea currents). However, the mission with the use of an UAV should take place in appropriate meteorological conditions, i.e., no precipitation, windless weather (wind speed not exceeding 6–7 m/s), sunny day. Full article

In this project report, the main outcomes relevant to the Sino-European Dragon-4 cooperation project ID 32235 “Microwave satellite measurements for coastal area and extreme weather monitoring” are reported. The project aimed at strengthening the Sino-European research cooperation in the exploitation of European Space Agency, Chinese and third-party mission Earth Observation (EO) microwave satellite data. The latter were exploited to perform an effective monitoring of coastal areas, even under extreme weather conditions. An integrated multifrequency/polarization approach based on complementary microwave sensors (e.g., Synthetic Aperture Radar, scatterometer, radiometer), together with ancillary information coming from independent sources, i.e., optical imagery, numerical simulations and ground measurements, was designed. In this framework, several tasks were addressed including marine target detection, sea pollution, sea surface wind estimation and coastline extraction/classification. The main outcomes are both theoretical (i.e., new models and algorithms were developed) and applicative (i.e., user-friendly maps were provided to the end-user community of coastal area management according to smart processing of remotely sensed data). The scientific relevance consists in the development of new algorithms, the effectiveness and robustness of which were verified on actual microwave measurements, and the improvement of existing methodologies to deal with challenging test cases. Full article

Wastewater treatment plants (WWTPs) in the City of New York, United States, are particularly vulnerable to frequent extreme weather events, including storm surges, high-intensity rainfall, and sea level rise, and are also affected by the cascade of these events. The complex structural configuration of WWTPs requires very fine-scale flood risk assessment, which current research has not pursued. We propose a robust technique to quantify the risk of inundations for the fourteen WWPTs through an automated sub-basin creation tool; 889 sub-basins were generated and merged with high-resolution building footprint data to create a comprehensive database for flood inundation analysis. The inundation depths and extents for the WWTPs and flood-prone regions were identified from hydrodynamic modeling of storm surge and sea level rise. The economic damage due to flooding for the WWTPs was also quantified using the HAZUS-MH model. Results indicated that the storm surges from various categories of hurricanes have the dominant impacts on flood depths around WWTPs, followed by high-intensity rainfall. Sea level rise was shown to have a relatively minor impact on flood depths. Results from economic damage analysis showed that the WWTPs are subjected to damage ranging from USD 60,000 to 720,000, depending on the size of the WWTP and the extremity of storm surge. The method of analyzing the inundation status of the research object through the sub-basin enables more accurate data to be obtained when calculating the runoff. It allows for a clearer view of the inundation status of the WWTPs when combined with the actual buildings. Using this database, predicting flood conditions of any extreme event or a cascade of extreme events can be conducted quickly and accurately. Full article

Submarine pipelines are the lifelines of the national economy. Under the influence of typhoons, high-speed currents and waves continuously erode the seabed, leading to suspension or even rupture of pipelines. Therefore, it is of great importance to study the sediment transport under the action of waves and currents. A numerical model of sediment scouring and deposition combining wave and currents is established, which considered tidal current, storm surges, wind waves, and sediments in the East China Sea. Combining with the monitoring of the actual laying condition of pipelines, it is found that the area with the most serious scouring is around KP300. It is shown that the typhoon weather with high intensity and density will lead to the suspension of pipelines, which is noteworthy in the construction of marine engineering. Full article

Aiming at the poor accuracy and difficult verification of maneuver modeling induced by the wind, waves and sea surface currents in the actual sea, a novel sea trials correction method for ship maneuvering is proposed. The wind and wave drift forces are calculated according to the measurement data. Based on the steady turning hypothesis and pattern search algorithm, the adjustment parameters of wind, wave and sea surface currents were solved, the drift distances and drift velocities of wind, waves and sea surface currents were calculated and the track and velocity data of the experiment were corrected. The hydrodynamic coefficients were identified by the test data and the ship maneuvering motion model was established. The results show that the corrected data were more accurate than log data, the hydrodynamic coefficients can be completely identified, the prediction accuracy of the advance and tactical diameters were 93% and 97% and the prediction of the maneuvering model was accurate. Numerical cases verify the correction method and full-scale maneuvering model. The turning circle advance and tactical diameter satisfy the standards of the ship maneuverability of International Maritime Organization (IMO). Full article

The backscattering coefficients measured by Ku-band scatterometers are strongly affected by rainfall, resulting in a systematic error in sea surface wind field retrieval. In rainy conditions, the radar signals are subject to absorption by the raindrops in their round-trip propagation through the atmosphere, while the backscatter of raindrops raises the echo energy. In addition, raindrops give rise to roughness by impinging the ocean surface, resulting in an increase in the echo energy measured by a scatterometer. Under moderate wind conditions, the comprehensive impact of rainfall causes the wind speeds retrieved by the scatterometer to be higher than their actual values. The HY-2A scatterometer is a Ku-band, pencil-beam, conically scanning scatterometer. To correct the systematic error of the HY-2A scatterometer measurement in rainy conditions, a neural network model is proposed according to the characteristics of the backscatter coefficients measured by the HY-2A scatterometer in the presence of rain. With the neural network, the wind fields of the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data were used as the reference to correct the deviation in backscatter coefficients measured by the HY-2A scatterometer in rainy conditions, and the accuracy in wind speeds retrieved using the corrected backscatter coefficients was significantly improved. Compared with the cases of wind retrieval without rain effect correction, the wind speeds retrieved from the corrected backscatter coefficients by the neural network show a much lower systematic deviation, which indicates that the neural network can effectively remove the systematic deviation in the backscatter coefficients and the retrieved wind speeds caused by rain. Full article

Most of the typhoons that impact coastal regions of Vietnam occur from the north to the central part, between June and November. As a result of global warming, typhoon intensities are expected to increase. Therefore, an assessment of various typhoon strengths is essential. In this study, Typhoon Lekima, which hit Vietnam in 2007, was simulated by weather research and forecast models, using ensemble simulation methodology. Reproductive results of the typhoon intensity are similar to actual estimated values from the Japan Meteorological Agency. Also, the variation of typhoon intensities and heavy rainfall in future climate scenarios was investigated using numerical simulations based on pseudo global warming conditions, constructed using fifth-phase results of the Coupled Model Intercomparison Project multi-model global warming experiments. Simulation results of five Pseudo Global Warming (PGW_FF) models indicate that intensities of the typhoon will be magnified in future climate. The minimum sea level pressure of typhoons similar to Typhoon Lekima in the future will increase from 8 hPa to 9 hPa, and the spatial distribution of maximum wind speed and tracked direction will move towards the southern regions. Total precipitation will significantly increase for a maximum of six hours, and the spatial distribution of heavy rain caused by typhoons will shift from the north to the southwest of Vietnam. In the future, simulated results showed that global warming correlates strongly with a significant increase in typhoon intensity and heavy rain. Full article