Comparison of Artificial Neural Network Models and Multiple Linear Regression Models in Cargo Port Performance Prediction

Abstract:

Cargo ports operational performance was specified typically through revenue earned, quantum of cargo handled and number of ships serviced. It was predisposed by infrastructural facilities and cargo handling rate; it had an effect over pre-berth waiting time of vessels waiting and berthing time of ships at a port. An Indian port’s ship movement and port operational characteristics had been studied for five years (2005-2009). Ship’s service time was the crucial parameter used to quantify the port performance. This paper focused on building an artificial neural network technique based model to illustrate the relationship between service time and port operational characteristics. Validations of ANN model, comparing multiple linear regression model outputs were reported.

• Periodical: Advanced Materials Research (Volumes 403-408)
• Pages: 3570-3577
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.403-408.3570
• Online since: November 2011
• Authors: P.Oliver Jayaprakash, K. Gunasekaran, S. Muralidharan
• Keywords: Artificial Neural Network (ANN), Berthing Time, MLR Analysis, Port Performance, Service Time, Ship Turnaround Time

References

[1] UNCTAD, Transport newsletter, no. 32, second quarter, Geneva. (2006).

[2] Wadhwa L.C., Capacity and Performance of bulk handling ports, Proceeding of Australian transportation research forum (vol. 15), Part I. (1990).

[3] Nathan huynh, c. Michael walton, and jeff davis, Finding the Number of Yard Cranes Needed to Achieve Desired Truck Turn Time at Marine Container Terminals, TRR: Journal of TRB, No. 1873, TRB., 2007, pp.98-108.

DOI: 10.3141/1873-12

[4] Haitham m. Al-deek, Which Method Is Better for Developing Freight Planning Models at Seaports—Neural Networks or Multiple Regressions?, TRR No. 1763, TRB., 2007, pp.90-97.

DOI: 10.3141/1763-14

[5] asypi Mokhtar & Dr. Muhammad Zar Sharh, A regression model for vessel turnaround time, Proceeding of Tokyo academy industry and cultural integration tour, 2006 pp.1-15.

[6] Kek C.C., Port Performance Indicator, Transportation, Water and Urban Development Department, The World Bank, Published Dec 1993, Transport No. PS-6, 1993, Annex A.

[7] De monie, g. , Unctad Monographs on Port Management 6 – Measuring and Evaluating Performance and Productivity, UNCTAD in collaboration with (IAPH), ICS), (OCIMF) & (IAPH). International Safety Guide for Oil Tankers and Terminals, V. ISBN-10 1856092917, (2006).

[8] Strandenes, S. P. And p. B. Marlow, Port Pricing and Competitiveness in Short Sea Shipping, International Journal of Transport Economics, 2000, vol. 27.

[9] Daniela Ambrosino & Elena Tànfani, A Discrete event simulation model for the analysis of Critical factors in the expansion plan of a marine container terminal, Proceedings 23rd European Conf. on Modelling and Simulation 2009, ISBN: 978-0-9553018-8-9.

DOI: 10.7148/2009-0288-0294

[10] Ng Siew Ming1 & Muhammad Zaly Shah, Petroleum Terminal's Operation Processes On Vessel Turnaround Time, EASTS International Symposium on Sustainable Transportation, 2008, Universiti Teknologi Malaysia.

[11] Yusuf Erzin and Isik Yilmaz , Multiple regression (MLR) and Artificial neural network models for prediction of soil suction, Geophysical research abstracts, vol. 12, (2010).

[12] K. Mohammadi, H.R. Eslami & sh. Dayyani dardashti , Comparison of regression, ARIMA and ANN models for Reservoir inflow forecasting using snowmelt equivalent (a case study of Karai. ), Journal of agricultural science and technology, vol. 7 2005, pp.17-30.

[13] Hsiao tien pao, A comparison of neural network and multiple regression analysis in modeling capital structure, Expert systems with applications: An international archive, vol. 35(3), 2008, Pergamon press. USA.

DOI: 10.1016/j.eswa.2007.07.018

[14] Silvia miguez, Guillem vallicrosa, Joaquim ciurana, Modelling manufacturing processes: a comparison between multiple regression analysis and the neural network approach, Int. journal of Mechatronics and Manufacturing systems, vol. 3, 2010, p.405424.

DOI: 10.1504/ijmms.2010.036066

[15] Arulsudar.N., Subramanian. N, Murthy. RS., Comparison of artificial neural network and multiple linear regression in the optimisation of formulation parameters of leuprolide acetate loaded liposomes, Journal of pharmaceutical sciences, vol. 2, 2005, pp.243-258.

[16] Andrew C. Comrie, comparing Neural networks and Regression models for Ozone forecasting, Journal of air &waste management association, Vol. 47, 1997, pp.653-663.

DOI: 10.1080/10473289.1997.10463925

[17] Mustafa E. Ureyen and Pelin gurkan, Comparison of artificial neural network and linear regression models for prediction of ring spun yarn properties. I. prediction of yarn tensile properties, Fibers and polymers, vol. 9-1, 2008. pp.87-91.

DOI: 10.1007/s12221-008-0014-4

[18] W. Grzesiak, R. Lacroix, J. Wojcik and P. Bszczyk, Acomparison of neural network and multiple regression predictions for 305 day lactation yield using partial lactation records, Canadian journal of animal science, vol. 83, (2003), pp.307-310.

DOI: 10.4141/a02-002

[19] Nghiep nguyen an dAl cripps, 'Predicting houing value: a comparison of multiple regression analysis and artificial neural networks, JRER, vol. 22(1), (2001).

[20] Port reform tool kit`, World Bank. Http: /www. ppiaf. org/ppiaf/sites/ppiaf. org.

DOI: 10.1596/0-8213-5470-1

[21] Ports of India website; Http: /www. portsofindia. nic. in.

[22] Position paper on The ports sector in India, 2009 Dept. Of economics affairs, Ministry of Finance, Government of India.