Towards a probabilistic model for estimation of grounding accidents in fluctuating backwater zone of the Three Gorges Reservoir

Jiang, Dan; Wu, Bing; Cheng, Zhaohai; Xue, Jie; Gelder, P.H.A.J.M. van

doi:10.1016/j.ress.2020.107239

Cited by 44 publications

(15 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the paper by author Jiang et al [19], an analytical model was developed that estimates the grounding probability using the Bayesian Network. Factors affecting grounding have been identified from historical data and previous studies based on systematic hazard analysis.…”

Section: Analytical Modelsmentioning

confidence: 99%

“…After obtaining the previous information and CPTs for the BN model, the results of modeling the assessment of the grounding probability in the test area are shown in Figure 3. After building the model and conducting the simulation, it was concluded that out of 26 factors, the area of the fluctuating backwater zone, the month of the year and the water level are the main factors behind the occurrence of grounding accidents in the Three Gorges Reservoir area [19].…”

mentioning

confidence: 99%

See 1 more Smart Citation

A Chronological Overview of Scientific Research on Ship Grounding Frequency Estimation Models

Galić

Lušić

Mladenović

et al. 2022

JMSE

View full text Add to dashboard Cite

In the global maritime industry, ship grounding represents about one-third of commercial groundings. Grounding is a type of accident where the hull of a ship strikes the seabed, causing damage to the hull. This type of accident can lead to damage to the hull and water penetration, but also to the pollution of the marine environment by the spillage of ship fuel or some liquid cargo carried by the ship. This paper presents a chronological overview of the contribution of ship grounding frequency, where one of the first ideas of grounding probability was presented by Macduff (1974) and Fuji (1974) in their models. Their ideas, such as grounding candidates and causing probability, laid the foundation for the development of future models. After their models, years later, other models continued to be developed; e.g., analytically (Macduff, Fujii, Amrowitcz, etc.) and statistically (Pedersen, Simonsen, etc.). To find the probability of grounding in an area of interest or in a scenario, it is necessary to have the number of grounding candidates and the causing probability. There are different internal and external factors that affect both probabilities, and in this paper, we analyze how various authors approach their grounding models and have tried to show the actual event as faithfully as possible and thus contribute to greater safety in maritime transport. The number of grounding frequency models significantly complicates their selection in practice; accordingly, the review of models in this paper should facilitate this, but also it should facilitate future research on this topic.

show abstract

Section: Analytical Modelsmentioning

confidence: 99%

mentioning

confidence: 99%

A Chronological Overview of Scientific Research on Ship Grounding Frequency Estimation Models

Galić

Lušić

Mladenović

et al. 2022

JMSE

View full text Add to dashboard Cite

show abstract

“…erefore, the established correlation degree model has a specific scope of application. For further research, we could introduce a comprehensive evaluation method such as the human factors analysis and classification system (HFACS) [48] to analyze the influencing factors from a systematic perspective, considering humans, vehicles, the environment, and management.…”

Section: Data Availabilitymentioning

confidence: 99%

Analysis of Freeway Safety Influencing Factors on Driving Workload and Performance Based on the Gray Correlation Method

Xie

Deng³

et al. 2021

Journal of Advanced Transportation

View full text Add to dashboard Cite

Human-related factors are a crucial inducement of traffic accidents. Understanding the influence of freeway environments on the driving behavior and workload experienced by drivers has been demonstrated to be of primary importance for improving traffic safety. To study the effect of alignment, traffic flow, and sign information on drivers’ mental workload and behavior, 16 scenarios were constructed using the orthogonal design method, and simulated driving experiments were carried out with 45 participants. During driving, indicators such as the mean and standard deviation of vehicle speed and lane departure were collected, and the NASA-TLX questionnaire was adopted to measure workload. Analysis of variance results indicated that the radius of the horizontal curve, gradient, flow, and sign information level have a significant influence on drivers’ workload and speed keeping ability. In addition, the horizontal curve radius has a significant effect on lane keeping ability. The importance of safety influencing factors on driving workload and performance was quantitatively ranked by integrating the trend of Deng’s correlation degree, comprehensive correlation degree, and similar correlation degree, whose weight was calculated using the entropy method. Traffic sign information was found to have the greatest impact on workload. In terms of driving performance, traffic volume has the greatest influence on the mean and standard deviation of vehicle speed, followed by the amount of sign information. Lane departure is most affected by the radius of the horizontal curve. These findings provide guidance for freeway traffic safety regulation, including workload control and road facility optimization.

show abstract

“…By combining the requirements for the light range in COLREGS and support vector classification to supervise and learn the actual meeting data, a map of the ship encounter azimuth division was constructed in [24]. An analytical model incorporating a Bayesian network is proposed to estimate the occurrence likelihood of a ship being grounded and collided in the fluctuating backwater zone in [25]. Ma et al [26] proposed a deep learning model for predicting the sailing intent in the intersection waterway because ship-ship collisions in such areas mainly occur by incorrectly interpreting the intents of other vessels.…”

Section: Ship Encounter Identificationmentioning

confidence: 99%

A Novel Adaptive Visual Analytics Framework for Multiship Encounter Identification

Rong

Shi

2021

Journal of Advanced Transportation

View full text Add to dashboard Cite

The automatic identification of multiship encounter is a vital criterion for ship collision avoidance and intelligent maritime safety surveillance. However, the parameters of ship encounter identification in the existing studies are fixed, and the methods are weak to give an automatic and visual performance in the multiship encounter identification. In order to fix the existed gap, this paper proposed a novel adaptive visual analytics framework for automatic multiship encounter identification based on density-based spatial clustering of applications with noise (DBSCAN) and visual analytics by adjusting the parameters of ship encounter adaptively. The DBSCAN clustering method was applied to detect the clusters of encounter ships and filter out the nonencounter ship, and the distribution and density of the encounter ship had been visualized on the nautical chart to give a better perception of ships’ behavior with a potentially high navigational risk. The framework had been designed and developed using DBSCAN and visual analytics, and the effectiveness was evaluated and validated by adjusting different parameters of multiship encounter within the Southwest waters of Zhoushan Island, China. The results showed that the proposed framework had a good performance in the visual identification of multiship encounter within confined waters, which could assist the ship collision avoidance and intelligent maritime surveillance system.

show abstract

Towards a probabilistic model for estimation of grounding accidents in fluctuating backwater zone of the Three Gorges Reservoir

Cited by 44 publications

References 49 publications

A Chronological Overview of Scientific Research on Ship Grounding Frequency Estimation Models

A Chronological Overview of Scientific Research on Ship Grounding Frequency Estimation Models

Analysis of Freeway Safety Influencing Factors on Driving Workload and Performance Based on the Gray Correlation Method

A Novel Adaptive Visual Analytics Framework for Multiship Encounter Identification

Contact Info

Product

Resources

About