LEE- Deniz Ulaştırma Mühendisliği-Doktora

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http://hdl.handle.net/11527/19252

Gözat

Konu "Deniz Trafik Düzeni Analizi" ile LEE- Deniz Ulaştırma Mühendisliği-Doktora'a göz atma
  • Öge
    Development of a dynamic navigational risk assessment model
    ( 2020) Şenol, Yunus Emre ; Arslan, Özcan ; 635623 ; Deniz Ulaştırma Mühendisliği Ana Bilim Dalı
    Marine traffic, which has an increasing importance in terms of global freight and passenger transportation, has increased significantly in recent years and has brought some navigational safety problems. An increase was observed especially in collision and grounding accidents in especially dense waterways. In order to find solutions to this problem, many academic studies have been carried out that offer different sights and analysis methods. In the literature review stage made within the scope of the thesis, the studies on the subject were examined in detail, the factors included to calculations, the methods utilised and their applicability to the solution of the problem were evaluated. Although the studies in the literature constitute an academic value in terms of their proposed methods and approaches, it has been evaluated that many of them are insufficient in terms of applicability, namely the solution of the problem faced by the industry. As a matter of fact, there is no real-time dynamic risk analysis algorithm that can work onboard ship which is capable of corresponding the needs of the industry. In addition, many studies in the literature do not seem to address both the risks of collision and grounding at the same time. Studies in which only collision or grounding risk analysis was presented could not fully meet the expectations of the maritime sector. For this reason, it is aimed to develop a real-time dynamic risk analysis algorithm with some novel and strong aspects which can provide decision support to the officer on watch, can work integrated with real navigational equipment. The proposed algorithm consists of 4 main stages as Automatic Identification System (AIS) Module where AIS data are decode and parsed, Electronic Navigational Chart (ENC) Module that allows reading ENCs, Calculation Module where all risks and other required calculations are performed, and Visualisation Module where risk indicators are projected with AIS targets over the visualized ENCs. The National Marine Electronics Association (NMEA) 0183 infrastructure, which is the standard data exchange protocol of ship navigation equipment, has been added to the algorithm so that it can be integrated to navigation equipment for real-time calculations. All of the factors obtained from integrated navigation equipment used as data source and which may affect the risk of collision and grounding were included directly or indirectly as inputs. Information of Closest Point of Approach (CPA), Time to Closest Point of Approach (TCPA), relative bearing, relative speed, ship's length and ship's type are determined as the system inputs of the algorithm. Own ship and target ships perceived with AIS data are not considered as a single point as in the classical approaches in the literature. Instead, the actual dimensions of the ships are calculated by considering the position information of the Global Positioning System (GPS) receiver sent by OTS on the ship. Ship forms created in real dimensions are perceived as a set of multi-points consisting of points in which a distance of less than 10 meters between each one, and risk calculations of collision is carried out in real time by including all of these points in consideration. Similarly, shallow contour information obtained using ENC, which is dangerous in terms of vessel draft value, is perceived as a set of multi-points with a distance of less than 10 meters between them. Risk calculations have been conducted with the Fuzzy Inference System (FIS) method, which is widely used as one of artificial intelligence methods, from medicine to many branches of engineering. A case study was carried out by applying the AIS data of a ship navigating in the Istanbul Strait to the model. In this study, it is aimed to develop a model to reduce the risks of collision and grounding by increasing situational awareness and thus providing a decision support.