A Statistical modellingapproaches on tidal analysis and  forecasting

Firdaus Mohamad Hamzah

QR Code Link :
Type :	article
Subject :	Q Science (General)
Main Author :	Firdaus Mohamad Hamzah
Additional Authors :	Hazrina Tajudin Mohd Khairul Amri Kamarudin Norazman Arbin Siti Hawa Mohd Yusof
Title :	A Statistical modellingapproaches on tidal analysis and forecasting

Place of Production :	Tanjong Malim
Publisher :	Fakulti Sains dan Matematik
Year of Publication :	2020
Corporate Name :	Universiti Pendidikan Sultan Idris
PDF Full Text :	Login required to access this item.

Abstract : Universiti Pendidikan Sultan Idris

Increase in the number of population in the lowelevation coastal zone has increase the importanceto reduce the risk of coastal and nuisance flooding, especially during high tide. This study attempts to generate forecast of high tide data using several statistical approaches such as seasonal naive model, Holt-Winter, Theta method and seasonal autoregressive and moving average method. Based on the decomposition plot using additive components of the time series, there are seasonal components in each data sets and increasing trend can be observed at Permatang Sedepa and Bagan Datuk, while decline follows by slowly increasing trend can be seen at Pelabuhan Klang station.Among all methods applied to the time series data,Theta method gives the lowest error for Pelabuhan and Permatang Sedepa with accuracy of 0.0403 and 0.0457 respectively, while Holt-Winter method gives the lowest error for high tide data at Bagan Datuk with accuracy of 0.0456. Malaccastraits serves various purposes including shipping, especially at Pelabuhan Klang, High number of activities in the area had caused unexpected outcome such as land subsidence, coastal erosion,deterioration of natural and man-made barrier, floods and inundation of land which indirectlyinfluences the physical of the port.

References

[1] W. Fang et al., “Examining the applicability ofdifferent sampling techniques in the development of decomposition-based streamflow forecasting models,” J. Hydrol., vol. 568, pp. 534–550, 2019. https://doi.org/10.1016/j.jhydrol.2018.11.020

[2] B. Neumann, A. T. Vafeidis, J. Zimmermann, and R. J. Nicholls, “Future coastal population growth and exposure to sea-level rise andcoastal flooding - A global assessment,” PLoS One, vol. 10, no. 3, 2015.

[3] S. Hallegatte, C. Green, R. J. Nicholls, and J. Corfee-Morlot, “Future flood losses in major coastal cities,” Nat. Clim. Chang., vol. 3, no. 9, pp. 802–806, 2013.

[4] H. R. Moftakhari, A. AghaKouchak, B. F. Sanders, and R. A. Matthew, “Cumulative hazard: The case of nuisance flooding,” Earth’s Futur., vol. 5, no. 2, pp. 214–223, 2017.

[5] M. K. Singla, J. Gupta, and P. Nijhawan, “Comparative Study on Backpropagation and Levenberg Marquardt Algorithm on Short Term Load Forecasting,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 8, no. 2, pp. 194–202. https://doi.org/10.30534/ijatcse/2019/14822019

[6] R. Q. Wang, M. T. Stacey, L. M. M. Herdman, P. L. Barnard, and L. Erikson, “The Influence of Sea Level Rise on the Regional Interdependence of Coastal Infrastructure,” Earth’s Futur., vol. 6, no. 5, pp. 677–688, 2018.

[7] M. El-Diasty, S. Al-Harbi, and S. Pagiatakis, “Hybrid harmonic analysis andwavelet network model for sea water level prediction,” Appl. Ocean Res., vol. 70, pp. 14– 21, 2018. https://doi.org/10.1016/j.apor.2017.11.007

[8] C. Berrett et al., “Spatial Prediction of Sea Level Trends,” 2018.

[9] R. L. Sriver, R. J. Lempert, P. Wikman-Svahn, and K. Keller, Characterizing uncertain sealevel rise projections to support investment decisions, vol. 13, no. 2. 2018.

[10] J. Yang, D. S. Abbot, D. D. B. Koll, Y. Hu, and A. P. Showman, “Ocean Dynamics and the Inner Edge of the Habitable Zone for Tidally Locked Terrestrial Planets,” Astrophys. J., vol. 871, no. 1, p. 29, 2019.

[11] O. Vergara, R. Morrow, I. Pujol, G. Dibarboure, and C. Ubelmann, “Revised Global Wave Number Spectra From Recent Altimeter Observations,” J. Geophys. Res. Ocean., vol. 124, no. 6, pp. 3523–3537, 2019.

[12] Y. Quilfen and B. Chapron, “Ocean Surface Wave-Current Signatures From Satellite Altimeter Measurements,” Geophys. Res. Lett., vol. 46, no. 1, pp. 253–261, 2019.

[13] C. Makris, Y. Androulidakis, V. Baltikas, Y. Kontos, T. Karambas, and Y. Krestenitis, “HiReSS: Storm surge simulation model for the operational forecasting of sea level elevation and currents in marine areas with harbor works,” Proc. 1st Int. Sci. Conf. Des. Manag. Port Coast. Offshore Work., vol. 1, pp. 11–15, 2019.

[14] T. Ezer, “On the interaction between a hurricane, the Gulf Stream and coastal sea level,” Ocean Dyn., vol. 68, no. 10, pp. 1259–1272, 2018.

[15] Akhil Muhammad Salim, G. S. Dwarakish, Liju K. V., Justin Thomas, Gayathri Devi, and Rajeesh R., “Weekly prediction of tides using Neural networks,” in Procedia Engineering, 2015, vol. 116, pp. 678–682.

[16] T. L. Lee, “Back-propagation neural network for long-term tidal predictions,” Ocean Eng., vol. 31, no. 2, pp. 225–238, 2004.

[17] Z. Zhang, Q. Zhang, and V. P. Singh, “Univariate streamflow forecasting using commonly used data-driven models: literature review and case study,” Hydrol. Sci. J., vol. 63, no. 7, pp. 1091–1111, 2018.

[18] L. Parviz and K. Rasouli, “Development of Precipitation Forecast Model Based on Artificial Intelligence and Subseasonal Clustering,” J. Hydrol. Eng. Eng., vol. 24, no. 12, pp. 1–13, 2019.

[19] S. Mohanasundaram, G. Suresh Kumar, and B. Narasimhan, “A novel deseasonalized time series model with an improved seasonal estimate for groundwater level predictions,” H2Open J., vol. 2, no. 1, pp. 25–44, 2019. https://doi.org/10.2166/h2oj.2019.022

[20] H.-F. Yeh and H.-L. Hsu, “Stochastic Model for Drought Forecasting in the Southern Taiwan Basin,” Water, vol. 11, no. 10, p. 2041, 2019.

[21] D. T. Meshram, S. D. Gorantiwar, and N. Bake, “Forecasting of Air Temperature of Western Part of Maharashtra, India,” Int. J. Sci. Environ. Technol., vol. 8, no. 1, pp. 201–217, 2019.

[22] P. M. MaC?ira, F. L. C. Oliveria, and R. C. Souza, “Forecasting natural inflow energy series with multi-channel singular spectrum analysis and bootstrap techniques,” Int. J. Energy Stat., vol. 3, no. 1, pp. 1–17, 2015.

[23] V. Assimakopoulos and K. Nikolopoulos, “The theta model: A decomposition approach to forecasting,” Int. J. Forecast., vol. 16, no. 4, pp. 521–530, 2000.

[24] M. Valipour, “Long-term runoff study using SARIMA and ARIMA models in the United States,” Meteorol. Appl., 2015.

[25] Y. H. T. Louis, Kuok King Kuok, Monzur Imteaz, Wai Yan Lai, and Derrick Kuok Xiong Ling, “Development of whale optimization neural network for daily water level forecasting,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 8, no. 3, pp. 354–362, May 2019.https://doi.org/10.30534/ijatcse/2019/04832019

This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials.
You may use the digitized material for private study, scholarship, or research.

Back to previous page