Meteorological Risk Assessment for Ships with Fuzzy Logic Designer
Fuzzy Logic, an advanced method to support decision-making, is used by various scientists in many disciplines. Fuzzy programming is a product of fuzzy logic, fuzzy rules, and implication. In marine science, fuzzy programming for ships is dramatically increasing together with autonomous ship studies. In this paper, a program to support the decision-making process for ship navigation has been designed. The program is produced in fuzzy logic and rules, by taking the marine accidents and expert opinions into account. After the program was designed, the program was tested by 46 ship accidents reported by the Transportation Safety Investigation Center of Turkey. Wind speed, sea condition, visibility, day/night ratio have been used as input data. They have been converted into a risk factor within the Fuzzy Logic Designer application and fuzzy rules set by marine experts. Finally, the expert's meteorological risk factor for each accident is compared with the program's risk factor, and the error rate was calculated. The main objective of this study is to improve the navigational safety of ships, by using the advance decision support model. According to the study result, fuzzy programming is a robust model that supports safe navigation.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 428
 C. Ducruet, S. Cuyala, A. El Hosni, Maritime networks as systems of cities: The long-term interdependencies between global shipping flows and urban development (1890–2010), Journal of Transport Geography 66 (2018) 340-355.
 T.-O. Nævestad, K.V. Størkersen, A. Laiou, G. Yannis, Framework conditions of occupational safety: Comparing Norwegian maritime cargo and passenger transport, International Journal of Transportation Science and Technology 7(4) (2018) 291-307.
 S. Niavis, T. Papatheochari, T. Kyratsoulis, H. Coccossis, Revealing the potential of maritime transport for ‘Blue Economy’ in the Adriatic-Ionian Region, Case Studies on Transport Policy 5(2) (2017) 380-388.
 P. Zhang, L. Drumm, The German Shipping Foundation: Has it been effective in maintaining maritime expertise in Germany?, Marine Policy (2020) 103871.
 O. Ellingsen, K.E. Aasland, Digitalizing the maritime industry: A case study of technology acquisition and enabling advanced manufacturing technology, Journal of Engineering and Technology Management 54 (2019) 12-27.
 M. Luo, S.-H. Shin, Half-century research developments in maritime accidents: Future directions, Accident Analysis & Prevention 123 (2019) 448-460.
 A. Toffoli, J.M. Lefevre, E. Bitner-Gregersen, J. Monbaliu, Towards the identification of warning criteria: Analysis of a ship accident database, Applied Ocean Research 27(6) (2005) 281-291.
 T. Brcko, J. Svetak, Fuzzy Reasoning as a Base for Collision Avoidance Decision Support System, Promet-Traffic & Transportation 25(6) (2013) 555-564.
 L.P. Perera, J.P. Carvalho, C.G. Soares, Fuzzy-logic based parallel collisions avoidance decision formulation for an Ocean Navigational System, IFAC Proceedings Volumes 43(20) (2010) 260-265.
 C.T. Cai, C.S. Yang, Q.D. Zhu, Y.H. Liang, Ieee, A fuzzy-based collision avoidance approach for multi-robot systems, Ieee, New York, 2007.
 N.A. Sedova, V.A. Sedov, R.I. Bazhenov, The Neural-Fuzzy Approach as a Way of Preventing a Maritime Vessel Accident in a Heavy Traffic Zone, Advances in Fuzzy Systems (2018) 8.
 B. Wu, T.L. Yip, X. Yan, C. Guedes Soares, Fuzzy logic based approach for ship-bridge collision alert system, Ocean Engineering 187 (2019) 106152.
 F. Goerlandt, J. Montewka, V. Kuzmin, P. Kujala, A risk-informed ship collision alert system: Framework and application, Saf. Sci. 77 (2015) 182-204.
 V.S. Nguyen, N.K. Im, Automatic Ship Berthing Based on Fuzzy Logic, International Journal of Fuzzy Logic and Intelligent Systems 19(3) (2019) 163-171.
 S.L. Kao, K.T. Lee, K.Y. Chang, M.D. Ko, A fuzzy logic method for collision avoidance in Vessel Traffic Service, Journal of Navigation 60(1) (2007) 17-31.
 J.-F. Balmat, F. Lafont, R. Maifret, N. Pessel, MAritime RISk Assessment (MARISA), a fuzzy approach to define an individual ship risk factor, Ocean Engineering 36(15) (2009) 1278-1286.
 J.-F. Balmat, F. Lafont, R. Maifret, N. Pessel, A decision-making system to maritime risk assessment, Ocean Engineering 38(1) (2011) 171-176.
 L.A. Zadeh, Fuzzy logic—a personal perspective, Fuzzy Sets and Systems 281 (2015) 4-20.
 CHAPTER 1 - Introduction to Fuzzy Systems, in: T. Terano, K. Asai, M. Sugeno (Eds.), Applied Fuzzy Systems, Academic Press1989, pp. 1-7.
 Chapter 2 - Basic Notions in Fuzzy Set Theory, in: D. Dubois, H. Prade, R.R. Yager (Eds.), Readings in Fuzzy Sets for Intelligent Systems, Morgan Kaufmann1993, pp. 21-26.
 A.M. Ibrahim, Chapter 3 - Fuzzy relations, in: A.M. Ibrahim (Ed.), Fuzzy Logic for Embedded Systems Applications, Newnes, Burlington, 2004, pp. 53-67.
 L.A. Bacci, L.G. Mello, T. Incerti, A. Paulo de Paiva, P.P. Balestrassi, Optimization of combined time series methods to forecast the demand for coffee in Brazil: A new approach using Normal Boundary Intersection coupled with mixture designs of experiments and rotated factor scores, International Journal of Production Economics 212 (2019) 186-211.