Fuel consumption of marine vessels plays an important role in both generating air pollution and ship operational expenses where the global environmental concerns toward air pollution and economics of shipping operation are being increased. In order to optimize ship fuel consumption, the fuel consumption prediction for her envisaged voyage is to be known. To predict fuel consumption of a ship, noon report (NR) data are available source to be analysed by different techniques. Because of the possible human error attributed to the method of NR data collection, it involves risk of possible inaccuracy. Therefore, in this study, to acquire pure valid data, the NR raw data of two very large crude carriers (VLCCs) composed with their respective Automatic Identification System (AIS) satellite data. Then, well-known models i.e. K-Mean, Self-Organizing Map (SOM), Outlier Score Base (OSB) and Histogram of Outlier Score Base (HSOB) methods are applied to the collected tankers NR during a year. The new enriched data derived are compared to the raw NR to distinguish the most fitted methodology of accruing pure valid data. Expected value and root mean square methods are applied to evaluate the accuracy of the methodologies. It is concluded that measured expected value and root mean square for HOSB are indicating high coherence with the harmony of the primary NR data.
Bole A. Radar and ARPA Manual. Chapter 5 – Automatic Identification System (AIS); 2014.
Fagerholt K, Laporte G, Norstad I. Reducing fuel emissions by optimizing speed on shipping routes. Journal of the Operational Research Society. 2010;61(3): 523-529.
Man Diesel & Turbo. Costs and Benefits of LNG as ship fuel for container Vessels. Engineering the Future; 2011.
Wang S, Meng Q. Bunker consumption optimization methods in shipping: A critical review and extensions. Transportation Research Part E: Logistics and Transportation Review. 2013;53: 49-62.
Kontovas C, Psaraftis HN. Reduction of emissions along the maritime intermodal containerchain: operational models and policies. Maritime Policy & Management. 2011. p. 451-469.
Kim JG, Kim HJ, Lee PTW. Optimizing ship speed to minimize fuel consumption. The International Journal of Transportation Research. 2014. p. 109-117.
Safaei AA, Ghassemi H, Ghiasi M. A Voyage Optimization for a Very Large Crude Carrier Oil Tanker: A Regional Voyage Case Study. Scentific Journal of Maritime University of Szczecin. 2015;44(116): 83-89.
Meng Q, Wang S. Optimal operating strategy for a longhaul liner service route. European Journal of perational Research. 2011;215(1): 105-114.
Notteboom T, Vernimmen B. The effect of high fuel costs on liner service conﬁguration in container shipping. Journal of Transport Geography. 2009;17(5): 325-337.
Nie Y, Wu X. Shortest path problem considering ontime arrival probability. Transportation Research Part B: Methological. 2009;43(6): 597-613.
Meng Q, Du Y, Wang Y. Shipping Log Data Based Container Ship Fuel Efficiency Modeling. Transportation Research Part B: Methological. 2016;83: 207-229.
Fang MC, Lin YH. The optimization of ship weather-routing algorithm based on the composite influence of multi-dynamic elements (II): Optimized routings. Applied Ocean Research. 2015;50: 130-140.
Lusic Z, Kos S, Galic S. Standardization of Plotting Courses and Selecting Turning Points Maritime Navigation. Promet – Traffic & Transportation. 2014;26(4): 313-322.
Vijendra S, Shivani P. Robust Outlier Detection Technique in Data Mining: A Univariate Approach. Computer Vision and Pattern Recognition Journal. 2014.
Williams G, Baxter R, He H, Hawkins S, Gu L. A Comparative Study for RNN for Outlier Detection in Data Mining. In: Proceedings of the 2nd IEEE International Conference on Data mining, 9-12 Dec 2002, Maebashi City, Japan; 2002.
Kantardzic M. Data mining concepts, models, methods, and algorithms. 2nd ed. Johan Wiley & Sons, Inc; 2011.
Han J, Kamber M, Pei J. Data mining concepts and techniques. 3rd ed. Morgan Kaufmann Publishers; 2006.
Ghosh-Dastidar B, Schafer JL. Outlier Detection and Editing Procedures for Continuous Multivariate Data. ORP Working Papers, Working Paper No. 2003-07, 2003.
Safaei AA, Ghassemi H, Ghiasi M. Correcting and Enriching Vessel’s Noon Report Data Using Statistical and Data Mining Methods. European Transport. 2018: no 67.
Kriegel HP, Schubert E, Zimek A. The art of runtime evaluation: Are we comparing algorithms or implementations?. Knowledge and Information System. 2016;52(2): 341-378.
Kind A, Stoecklin M, Dimitropoulos X. Histogram Based Traffic Anomaly Detection. IEEE Transaction on Network and Services Management. 2009;6(2): 110-121.