An artificial neural network-source apportionment-based prediction model for carbon monoxide from total number of ships calling by ports in Malaysia

Shazlyn Milleana Shaharudin

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Type :	Article
Subject :	Q Science (General)
ISBN :	0941-0643
Main Author :	Shazlyn Milleana Shaharudin
Title :	An artificial neural network-source apportionment-based prediction model for carbon monoxide from total number of ships calling by ports in Malaysia
Hits :	24

Place of Production :	Tanjung Malim
Publisher :	Fakulti Sains & Matematik
Year of Publication :	2024
Notes :	Neural Computing and Applications
Corporate Name :	Universiti Pendidikan Sultan Idris
HTTP Link :	Click to view web link
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Abstract : Universiti Pendidikan Sultan Idris

Air pollution has been a significant issue in recent years due to rising industrialization and maritime activity around the globe, making air pollution forecasting a crucial concept in environmental study. This prompted the deployment of principal component analysis (PCA) for the source apportionment amongst the air quality parameters and the artificial neural network (ANN) for the prediction of the significant air quality parameters in ports area for this study. The study was carried out in seven federal ports across Malaysia for the period of 2009 and 2018, and 14 air quality parameters were calculated using information on air quality acquired from the Department of the Environment. The results of the study showed PCA identified NOx, NO, SO, NO2, CO, and PM10 as the variables of significance with a variation of 44.31% with CO exhibiting the highest factor loading (0.968). Artificial Neural Network-Source Apportionment accurately predicted CO as the major pollutant with R2 in training (0.7492) and validation (0.7492). This study has successfully established a connection between the source of apportionment of air pollutant parameters and the total number of ships, as well as an effective alternative tool for predicting the most significant air quality air pollutant parameters in Malaysian ports, which can be applied in other regions to comprehend ship emission trends. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

References

Smith TW, Jalkanen JP, Anderson BA, Corbett JJ, Faber J, Hanayama S, O’Keeffe E, Parker S, Johansson L, Aldous L, Raucci C. (2014). Third imo ghg study 2014. International maritime Organisation (IMO), London, UK, 2014.

Saputra H, Maimun A, Koto J (2013) Estimation and distribution of exhaust ship emission from marine traffic in the straits of Malacca and Singapore using Automatic Identification System (AIS) data. Jurnal Mekanikal.

Mihmanli E (2011) Research Report: Combating Piracy in the Strait of Malacca.

Toscano D, Murena F (2019) Atmospheric ship emissions in ports: a review. Correlation with data of ship traffic. Atmos Environ X 4:100050.

Song SK, Shon ZH, Kim YK, Kang YH, Oh IB, Jung CH (2010) Influence of ship emissions on ozone concentrations around coastal areas during summer season. Atmos Environ 44(5):713–723.

Awang NR, Elbayoumi M, Ramli NA, Yahaya AS (2016) Diurnal variations of ground-level ozone in three port cities in Malaysia. Air Qual Atmos Health 9(1):25–39.

Li Q, Jacob DJ, Bey I, Palmer PI, Duncan BN, Field BD, Martin RV, Fiore AM, Yantosca RM, Parrish DD, Simmonds PG (2002) Transatlantic transport of pollution and its effects on surface ozone in Europe and North America. J Geophys Res Atmos 107(D13):ACH4.

Deniz C, Durmus¸og˘lu Y (2008) Estimating shipping emissions in the region of the Sea of Marmara. Turkey Sci Total Environ 390(1):255–261.

Han CH (2010) Strategies to reduce air pollution in shipping industry. Asian J Shipp Logist 26(1):7–29.

IMO (2009) Second IMO GHG Study 2009. International Maritime Organisation, London.

IMO (2014) Third IMO GHG Study 2014. International Maritime Organisation, London.

Corbett JJ, Koehler HW (2003) Updated emissions from ocean shipping. J Geophys Res Atmos 108(D20).

Eyring V, Ko¨hler HW, Van Aardenne J, Lauer A (2005) Emissions from international shipping: 1. The last 50 years. J Geophys Res Atmos 110(D17).

Dominick D, Juahir H, Latif MT, Zain SM, Aris AZ (2012) Spatial assessment of air quality patterns in Malaysia using multivariate analysis. Atmos Environ 60:172–181.

Sudeshana P (2013) modelling of ambient for SOX and NOX pollutants through artificial neural network in sensitive area of Ujjain city. Int J Adv Biotechnol Res 4(4):476–480.

Kamal MM, Jailani R, Shauri RLA (2006) Prediction of ambient air quality based on neural network technique. In 2006 4th Student Conference on Research and Development (pp. 115–119).IEEE.

Ul-Saufie AZ, Yahya AS, Ramli NA, Hamid HA (2011) Comparison between multiple linear regression and feed forward back propagation neural network models for predicting PM10 concentration level based on gaseous and meteorological parameters. Int J Appl 1(4):42–49.

Abdullah S, Ismail M, Ahmed AN, Abdullah AM (2019) Forecasting particulate matter concentration using linear and nonlinear approaches for air quality decision support. Atmosphere 10(11):667.

Azid A, Juahir H, Toriman ME, Kamarudin MKA, Saudi ASM, Hasnam CNC, Aziz NAA, Azaman F, Latif MT, Zainuddin SFM, Osman MR (2014) Prediction of the level of air pollution using principal component analysis and artificial neural network techniques: a case study in Malaysia. Water Air Soil Pollut 225(8):1–14.

Azid A, Juahir H, Latif MT, Zain SM, Osman MR (2013) Feedforward artificial neural network model for air pollutant index prediction in the southern region of Peninsular Malaysia. J Environ Protect.

Azid A, Juahir H, Aris AZ, Toriman ME, Latif MT, Zain SM, Yusof KMKK, Saudi ASM (2014) Spatial analysis of the air pollutant index in the Southern Region of Peninsular Malaysia using Environmetric Techniques. In From sources to solution (pp.307–312). Springer, Singapore.

Azid A, Juahir H, Amran MA, Suhaili Z, Osman MR, Muhamad A, Abidin IZ, Sulaiman NH, Saudi ASM (2015) Spatial air quality modelling using chemometrics techniques: a case study in Peninsular Malaysia. Malays J Anal Sci 19(6):1415–1430.

Isiyaka HA, Azid A (2015) Air quality pattern assessment in Malaysia using multivariate techniques. Malays J Anal Sci 19(5):966–978.

AhmadIsiyaka H, Juahir H, Toriman ME, Gasim BM, Azid A, Amri MK, Ibrahim A, Usman UN, Rano AR, Garba MA (2014) Spatial assessment of air pollution index using environ metric modelling techniques. Adv Environ Biol 8(24):244–256.

Yu W, Lei B, Ng MK, Cheung AC, Shen Y, Wang S (2021) Tensorizing GAN with high-order pooling for Alzheimer’s disease assessment. IEEE Trans Neural Netw Learn Syst 33(9):4945–4959.

Hu S, Lei B, Wang S, Wang Y, Feng Z, Shen Y (2021) Bidirectional mapping generative adversarial networks for brain MR to PET synthesis. IEEE Trans Med Imaging 41(1):145–157.

You S, Lei B, Wang S, Chui CK, Cheung AC, Liu Y, Gan M, Wu G, Shen Y (2022) Fine perceptive gans for brain mr image superresolution in wavelet domain. IEEE Trans Neural Netw Learn Syst.

Mater Y, Kamel M, Karam A, Bakhoum E (2023) ANN-Python prediction model for the compressive strength of green concrete. Constr Innov 23(2):340–359.

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