Modelling of an efficient system for prediction ships' estimated time of arrival using artificial neural network

Md. Raqibur Rahman; Ehtashamul Haque; Sadia Tasneem Rahman; Yaseen Ahmed; K. Habibul Kabir

doi:10.1007/978-981-19-7346-8_18

Modelling of an efficient system for prediction ships' estimated time of arrival using artificial neural network

Md. Raqibur Rahman
, Ehtashamul Haque
, Sadia Tasneem Rahman
, Yaseen Ahmed
, K. Habibul Kabir

Naval Architecture, Ocean And Marine Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

6 Citations (Scopus)

Abstract

Ports act as a hub to the global economy. As such, port efficiency is an important factor that has to be maintained properly. A smart port system aims to increase port efficiency by integrating state-of-the-art technology with port management and predicting a ship's estimated time of arrival (ETA) is a critical step towards establishing a smart port system. This study aims to develop a data-driven model to estimate the ETA of incoming ships to Port Klang of Malaysia based on past voyage data. An artificial neural network (ANN)-based model to predict ETA has been proposed in the study. The proposed model achieves a mean absolute percentage error (MAPE) value of 36.99% with a mean absolute error (MAE) value of 4603.1367 s. The model's coefficient of determination was calculated to be 78.67% indicating a satisfactory fit to the data set.

Original language	English
Title of host publication	Computational Intelligence
Subtitle of host publication	Select Proceedings of InCITe 2022
Editors	Anupam Shukla, B.K. Murthy, Nitasha Hasteer, Jean-Paul Van Belle
Place of Publication	Singapore
Publisher	Springer
Pages	199-206
Number of pages	8
Volume	968
ISBN (Electronic)	9789811973468
ISBN (Print)	9789811973451
DOIs	https://doi.org/10.1007/978-981-19-7346-8_18
Publication status	Published - 16 Feb 2023

Publication series

Name	Lecture Notes in Electrical Engineering
Publisher	Springer
Volume	968

Keywords

smart port
estimated time of arrival
artificial neural network
4th industrial revolution

Access to Document

10.1007/978-981-19-7346-8_18

Cite this

Rahman, M. R., Haque, E., Tasneem Rahman, S., Ahmed, Y., & Habibul Kabir, K. (2023). Modelling of an efficient system for prediction ships' estimated time of arrival using artificial neural network. In A. Shukla, B. K. Murthy, N. Hasteer, & J.-P. Van Belle (Eds.), Computational Intelligence: Select Proceedings of InCITe 2022 (Vol. 968, pp. 199-206). (Lecture Notes in Electrical Engineering; Vol. 968). Springer. https://doi.org/10.1007/978-981-19-7346-8_18

Rahman, Md. Raqibur ; Haque, Ehtashamul ; Tasneem Rahman, Sadia et al. / Modelling of an efficient system for prediction ships' estimated time of arrival using artificial neural network. Computational Intelligence: Select Proceedings of InCITe 2022. editor / Anupam Shukla ; B.K. Murthy ; Nitasha Hasteer ; Jean-Paul Van Belle. Vol. 968 Singapore : Springer, 2023. pp. 199-206 (Lecture Notes in Electrical Engineering).

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title = "Modelling of an efficient system for prediction ships' estimated time of arrival using artificial neural network",

abstract = "Ports act as a hub to the global economy. As such, port efficiency is an important factor that has to be maintained properly. A smart port system aims to increase port efficiency by integrating state-of-the-art technology with port management and predicting a ship's estimated time of arrival (ETA) is a critical step towards establishing a smart port system. This study aims to develop a data-driven model to estimate the ETA of incoming ships to Port Klang of Malaysia based on past voyage data. An artificial neural network (ANN)-based model to predict ETA has been proposed in the study. The proposed model achieves a mean absolute percentage error (MAPE) value of 36.99\% with a mean absolute error (MAE) value of 4603.1367 s. The model's coefficient of determination was calculated to be 78.67\% indicating a satisfactory fit to the data set.",

keywords = "smart port, estimated time of arrival, artificial neural network, 4th industrial revolution",

author = "Rahman, \{Md. Raqibur\} and Ehtashamul Haque and \{Tasneem Rahman\}, Sadia and Yaseen Ahmed and \{Habibul Kabir\}, K.",

year = "2023",

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Rahman, MR, Haque, E, Tasneem Rahman, S, Ahmed, Y & Habibul Kabir, K 2023, Modelling of an efficient system for prediction ships' estimated time of arrival using artificial neural network. in A Shukla, BK Murthy, N Hasteer & J-P Van Belle (eds), Computational Intelligence: Select Proceedings of InCITe 2022. vol. 968, Lecture Notes in Electrical Engineering, vol. 968, Springer, Singapore, pp. 199-206. https://doi.org/10.1007/978-981-19-7346-8_18

Modelling of an efficient system for prediction ships' estimated time of arrival using artificial neural network. / Rahman, Md. Raqibur; Haque, Ehtashamul; Tasneem Rahman, Sadia et al.
Computational Intelligence: Select Proceedings of InCITe 2022. ed. / Anupam Shukla; B.K. Murthy; Nitasha Hasteer; Jean-Paul Van Belle. Vol. 968 Singapore: Springer, 2023. p. 199-206 (Lecture Notes in Electrical Engineering; Vol. 968).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

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AU - Rahman, Md. Raqibur

AU - Haque, Ehtashamul

AU - Tasneem Rahman, Sadia

AU - Ahmed, Yaseen

AU - Habibul Kabir, K.

PY - 2023/2/16

Y1 - 2023/2/16

N2 - Ports act as a hub to the global economy. As such, port efficiency is an important factor that has to be maintained properly. A smart port system aims to increase port efficiency by integrating state-of-the-art technology with port management and predicting a ship's estimated time of arrival (ETA) is a critical step towards establishing a smart port system. This study aims to develop a data-driven model to estimate the ETA of incoming ships to Port Klang of Malaysia based on past voyage data. An artificial neural network (ANN)-based model to predict ETA has been proposed in the study. The proposed model achieves a mean absolute percentage error (MAPE) value of 36.99% with a mean absolute error (MAE) value of 4603.1367 s. The model's coefficient of determination was calculated to be 78.67% indicating a satisfactory fit to the data set.

AB - Ports act as a hub to the global economy. As such, port efficiency is an important factor that has to be maintained properly. A smart port system aims to increase port efficiency by integrating state-of-the-art technology with port management and predicting a ship's estimated time of arrival (ETA) is a critical step towards establishing a smart port system. This study aims to develop a data-driven model to estimate the ETA of incoming ships to Port Klang of Malaysia based on past voyage data. An artificial neural network (ANN)-based model to predict ETA has been proposed in the study. The proposed model achieves a mean absolute percentage error (MAPE) value of 36.99% with a mean absolute error (MAE) value of 4603.1367 s. The model's coefficient of determination was calculated to be 78.67% indicating a satisfactory fit to the data set.

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Rahman MR, Haque E, Tasneem Rahman S, Ahmed Y, Habibul Kabir K. Modelling of an efficient system for prediction ships' estimated time of arrival using artificial neural network. In Shukla A, Murthy BK, Hasteer N, Van Belle JP, editors, Computational Intelligence: Select Proceedings of InCITe 2022. Vol. 968. Singapore: Springer. 2023. p. 199-206. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-19-7346-8_18

Modelling of an efficient system for prediction ships' estimated time of arrival using artificial neural network

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