Analysis of Ship Accidents in the Istanbul Strait Using Neuro-Fuzzy and Genetically Optimised Fuzzy Classifiers

Erol, Sercan; Demir, Mengu; Çetişli, Bayram; Eyüboğlu, Ekrem

doi:10.1017/s0373463317000601

Cited by 20 publications

(17 citation statements)

References 39 publications

(63 reference statements)

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“…The positive coefficient associated with the heavy weather condition signifies that tugboat accidents that occurred in heavy weather are three times more likely to be serious. This result coincides with the studies examining container ship accidents (Talley, 1996), fishing vessel accidents (Jin, 2014), and ship accidents, irrespective of type, in the İstanbul Strait (Erol et al, 2018). These studies have revealed that the severity of marine accidents is greater in adverse weather conditions.…”

Section: Resultssupporting

confidence: 91%

Decision Tree and Logistic Regression Analysis to Explore Factors Contributing to Harbour Tugboat Accidents

2020

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As tugboats interact very closely with ships in restricted waters, the possibility of accidents increases in these operations. Despite the high accident possibility, there is a gap in studies on tugboat accidents. This study aims to analyse accidents involving tugboats using data mining. For this purpose, a tugboat accidents dataset consisting of a total of 496 accident records for the period from 2008 to 2019 was collected. Logistic regression and decision tree algorithms were implemented to the dataset. The results revealed that tugboat propulsion type is the most important and influential factor in the severity of tugboat accidents. The inferences drawn from these results could be beneficial for tugboat operators and port authorities in enhancing their awareness of the factors affecting tugboat accidents. In addition, the outputs of this study can be a reference for management units in developing strategies for preventing tugboat accidents and can also be used in effective planning for practicable prevention programmes and practices.

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Section: Resultssupporting

confidence: 91%

Decision Tree and Logistic Regression Analysis to Explore Factors Contributing to Harbour Tugboat Accidents

2020

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“…The torque control vector is used to eliminate nonlinearities from the nonlinear model (Erol et al, 2018) and to ensure the asymptotic stability of the tracking error. Consider the following control vector (Isidori, 1989):…”

Section: Mathematical Model Of a Set Of Usvsmentioning

confidence: 99%

“…The proposed solution can be used in decision support systems on board to generate a safe trajectory. Erol et al (2018) used a neuro-fuzzy approach and genetically optimized fuzzy classifiers to analyze marine accidents occurring inside narrow, shallow and busy waterways. The modified Wolf Pack algorithm is proposed by Zhang et al (2017) to overcome the slow convergence rate and to improve the accuracy of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Collision Avoidance Algorithm for Multiple Marine Surface Vehicles

Hedjar

Bounkhel

2019

International Journal of Applied Mathematics and Computer Science

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In recent years, unmanned surface vehicles have been widely used in various applications from military to civil domains. Seaports are crowded and ship accidents have increased. Thus, collision accidents occur frequently mainly due to human errors even though international regulations for preventing collisions at seas (COLREGs) have been established. In this paper, we propose a real-time obstacle avoidance algorithm for multiple autonomous surface vehicles based on constrained convex optimization. The proposed method is simple and fast in its implementation, and the solution converges to the optimal decision. The algorithm is combined with the PD-feedback linearization controller to track the generated path and to reach the target safely. Forces and azimuth angles are efficiently distributed using a control allocation technique. To show the effectiveness of the proposed collision-free path-planning algorithm, numerical simulations are performed.

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Section: Characteristics Of Traffic-intensive Areas Of the Water Tranmentioning

confidence: 99%

“…Owing to the coupling of multiple factors, ship navigation accidents occur frequently. Previous studies have indicated that ship accidents are closely connected to traffic-intensive areas [22,23]. Therefore, it is meaningful to analyze the characteristics of traffic-intensive areas of water transportation systems, and it is also beneficial to identify the key vulnerability factors affecting the water transportation system.…”

Section: Characteristics Of Traffic-intensive Areas Of the Water Tranmentioning

confidence: 99%

Identification and Analysis of Vulnerability in Traffic-Intensive Areas of Water Transportation Systems

Chen

Liu

Wan³

et al. 2019

JMSE

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Water transportation accidents have occurred frequently in recent years. In order to improve the emergency response capability of water transportation systems under traffic-intensive conditions, this paper identifies and analyzes the vulnerability in traffic-intensive areas of water transportation systems. Firstly, the vulnerability identification model was constructed based on the analysis of characteristics and the vulnerability-influencing factors of water transportation systems. The newly proposed model is composed of three parts including the DEMATEL (Decision Making Trial and Evaluation Laboratory) method, ISM (interpretative structural modeling) model, and AHP (Analytic Hierarchy Process)–entropy weight method. Finally, a case study of the Yangtze River was conducted to test the logicality and feasibility of the proposed model. The research results reveal that traffic flow density, ship traffic, tides, fog, and bad weather are the key factors affecting the vulnerability of water transportation in traffic-intensive areas of the Yangtze River estuary. However, the influence of navigation aid configuration, berth, anchorage, and obstruction on the system vulnerability is relatively lower. The findings of this study can provide helpful references for maritime administration authorities on the management of water transportation safety.

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Analysis of Ship Accidents in the Istanbul Strait Using Neuro-Fuzzy and Genetically Optimised Fuzzy Classifiers

Cited by 20 publications

References 39 publications

Decision Tree and Logistic Regression Analysis to Explore Factors Contributing to Harbour Tugboat Accidents

Decision Tree and Logistic Regression Analysis to Explore Factors Contributing to Harbour Tugboat Accidents

An Automatic Collision Avoidance Algorithm for Multiple Marine Surface Vehicles

Identification and Analysis of Vulnerability in Traffic-Intensive Areas of Water Transportation Systems

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