Sains Malaysiana 51(7)(2022): 2003-2012

http://doi.org/10.17576/jsm-2022-5107-05

Simulation and Analysis of Sea-Level Change from Tide Gauge Station by using Artificial Neural Network Models

(Simulasi dan Analisis Perubahan Aras Laut dari Stesen Tolok Air Pasang Surut dengan menggunakan Model Rangkaian Neural Buatan)

MILAD BAGHERI¹, ZELINA Z IBRAHIM², LATIFAH ABD MANAF², MOHD FADZIL AKHIR¹ & WAN IZATUL ASMA WAN TALAAT^1,*

¹Institute of Oceanography and Environment, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu Darul Iman, Malaysia

²Department of Environment, Faculty of Environmental and Forestry, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor Darul Ehsan, Malaysia

Received: 9 October 2021/Accepted: 23 December 2021

Abstract

Sea level change is one of the most certain results of global warming. Sea level change would increase erosion in coastal areas, result in intrusion into water supplies, inundate coastal marshes and other important habitats, and make the coastal property more vulnerable to erosion and flooding. This situation coincides with the massive socio-economic development of the coastal city areas. The coastal areas of the East Coast of Peninsular Malaysia are vulnerable to sea-level change, flooding, and extreme erosion events. The monthly Mean Sea Level (MSL) change was simulated by using two Artificial Neural Network (ANN) models, Feed Forward- Neural Network (FF-NN) and Nonlinear Autoregressive Exogenous- Neural Network (NARX-NN) models. Both models did well in recreating sea levels and their fluctuating patterns, according to the data. The NARX-NN model with architecture (5-6-1) and four lag options, on the other hand, got the greatest results. The findings of the model's mean sea level rise simulation show that Kuala Terengganu would have a growing and upward trend of roughly 25.34 mm/year. This paper shows that the eastern coast of Malaysia is highly vulnerable to sea-level rise and therefore, requires sustainable adaptation policies and plans to manage the potential impacts. It recommends that various policies, which enable areas to be occupied for longer before the eventual retreat, could be adapted to accommodate vulnerable settlements on the eastern coast of Malaysia.

Keywords: Climate change; coastal city; FF-NN; NARX-NN; tide gauge; time series analysis

Abstrak

Perubahan paras laut adalah salah satu hasil pemanasan global yang paling pasti. Perubahan paras laut akan meningkatkan hakisan di kawasan pantai, mengakibatkan pencerobohan ke dalam bekalan air, membanjiri paya pantai dan habitat penting lain dan menjadikan harta pantai lebih terdedah kepada hakisan dan banjir. Keadaan ini bertepatan dengan pembangunan sosio-ekonomi yang besar di kawasan bandar pantai. Kawasan pantai di Pantai Timur Semenanjung Malaysia terdedah kepada perubahan paras laut, banjir dan kejadian hakisan yang melampau. Perubahan Purata Aras Laut (MSL) bulanan telah disimulasikan dengan menggunakan dua model Rangkaian Neural Buatan (ANN), Rangkaian Neural Feed Hadapan (FF-NN) dan Model Rangkaian Neural Eksogen Autoregresif Tak Linear (NARX-NN). Kedua-dua model itu berjaya mencipta semula paras laut dan corak turun naiknya, menurut data. Model NARX-NN dengan seni bina (5-6-1) dan empat pilihan ketinggalan, sebaliknya, mendapat hasil terbaik. Penemuan simulasi kenaikan paras laut purata model menunjukkan bahawa Kuala Terengganu akan mempunyai aliran meningkat dan meningkat kira-kira 25.34 mm/tahun. Kertas ini mendedahkan bahawa pantai timur Malaysia sangat terdedah kepada kenaikan paras laut dan oleh itu, memerlukan dasar dan rancangan penyesuaian yang mampan untuk mengurus kesan yang berpotensi. Ia mengesyorkan bahawa pelbagai dasar, yang membolehkan kawasan diduduki lebih lama sebelum berundur akhirnya, boleh disesuaikan untuk menampung penempatan yang terdedah di pantai timur Malaysia.

Kata kunci: Analisis siri masa; bandar pantai; FF-NN; NARX-NN; perubahan iklim; tolok air pasang surut

REFERENCES

Amerian, Y. & Voosoghi, B. 2011. Least squares spectral analysis for detection of systematic behaviour of digital level compensator. Journal of Geodetic Science 1(1): 35-40.

Ariffin, E.H., Sedrati, M., Akhir, M.F., Yaacob, R. & Husain, M.L. 2016. Open sandy beach morphology and morphodynamic as response to seasonal monsoon in Kuala Terengganu, Malaysia. Journal of Coastal Research 75(10075): 1032-1036.

Awang, N.A. & Hamid, M.R.A. 2013. Sea level rise adaptation measures: Sea level rise in Malaysia. Hydrolink 2(1): 47-49.

Bagheri, M., Zaiton Ibrahim, Z., Mansor, S., Manaf, L.A., Akhir, M.F., Talaat, W.I.A.W. & Pour, A.B. 2021a. Land-use suitability assessment using Delphi and Analytical Hierarchy Process (D-AHP) hybrid model for coastal city management: Kuala Terengganu, Peninsular Malaysia. ISPRS International Journal of Geo-Information 10(9): 621-656.

Bagheri, M., Ibrahim, Z.Z., Akhir, M.F., Talaat, W.I.A.W., Oryani, B., Rezania, S., Wolf, I.D. & Pour, A.B. 2021b. Impacts of future sea-level rise under global warming assessed from tide gauge records: A case study of the east coast economic region of Peninsular Malaysia. Land 10(12): 1382-1406.

Bagheri, M., Zaiton Ibrahim, Z., Akhir, M.F., Talaat, W.I.A.W., Oryani, B., Rezania, S. & Pour, A.B. 2021c. Developing a climate change vulnerability index for coastal city sustainability, mitigation, and adaptation: A case study of Kuala Terengganu, Malaysia. Land 10(11): 1271-1294.

Bagheri, M., Zaiton Ibrahim, Z., Mansor, S., Manaf, L.A., Akhir, M.F., Talaat, W.I.A.W. & Pour, A.B. 2021d. Land-use suitability assessment using Delphi and Analytical Hierarchy Process (D-AHP) hybrid model for coastal city management: Kuala Terengganu, Peninsular Malaysia. ISPRS International Journal of Geo-Information 10(9): 621-645.

Bagheri, M., Ibrahim, Z., Bin Mansor, S., Abd Manaf, L., Badarulzaman, N. & Vaghefi, N. 2019. Shoreline change analysis and erosion prediction using historical data of Kuala Terengganu, Malaysia. Environmental Earth Sciences 78(15): 1-21.

Bagheri, M., Sulaiman, W.N.A. & Vaghefi, N. 2013. Application of geographic information system technique and analytical hierarchy process model for land-use suitability analysis on coastal area. Journal of coastal conservation 17(1): 1-10.

Ghamarnia, H. & Jalili, Z. 2015. Artificial network for predicting water uptake under shallow saline ground water conditions. Journal of Scientific Research and Reports 1(1): 359-372.

Hamzehie, M.E., Mazinani, S., Davardoost, F., Mokhtare, A., Najibi, H., Van der Bruggen, B. & Darvishmanesh, S. 2014. Developing a feed forward multilayer neural network model for prediction of CO₂ solubility in blended aqueous amine solutions. Journal of Natural Gas Science and Engineering 21(1): 19-25.

Ibrahim, N. & Wibowo, A. 2013. Predictions of water level in Dungun River Terengganu using partial least squares regression. International Journal of Basic Applied Sciences 12(1): 1-7.

IPCC. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II, and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Switzerland: Geneva.

Khamis, A. & Abdullah, S.N.S.B. 2014. Forecasting wheat price using Backpropagation and NARX Neural Network. The International Journal of Engineering and Science 3(11): 19-26.

Lan, Y.J., Hsu, T.W., Lin, Y.C. & Huang, C.J. 2013. An adaptation due to climate change in southwest coast of Taiwan. Coastal Management 41(2): 172-189.

Mashaly, A.F., Alazba, A.A., Al-Awaadh, A.M. & Mattar, M.A. 2015. Predictive model for assessing and optimizing solar still performance using artificial neural network under hyper arid environment. Solar Energy 118(1): 41-58.

Mimura, N. 2013. Sea-level rise caused by climate change and its implications for society. Proceedings of the Japan Academy, Series B, Physical and Biological Sciences 89(7): 281-301.

NAHRIM. 2010a. The Study of the Impact of Climate Change on Sea Level Rise in Malaysia (Final Report). National Hydraulic Research Institute Malaysia p. 172.

NAHRIM. 2010b. Proceedings of the National Seminar on Coastal Morphology (COSMO) the Muddy Coast of Malaysia (Final Report). National Hydraulic Research Institute Malaysia p. 220.

Nelson, J.G. & Serafin, R. 1996. Environmental and resource planning and decision making in Canada: A human ecological and a civics approach. Canada in Transition: Results of Environmental and Human Geographical Research 1(1): 1-25.

Polo, F.A.O., Bermejo, J.F., Fernández, J.F.G. & Márquez, A.C. 2015. Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models. Renewable Energy 81(1): 227-238.

Ranković, V., Novaković, A., Grujović, N., Divac, D. & Milivojević, N. 2014. Predicting piezometric water level in dams via artificial neural networks. Neural Computing and Applications 24(5): 1115-1121.

Tangang, F. 2007. Climate Change and Global Warming: Malaysia Perspective: Malaysia Perspective and Challenges. UKM Public Speech, Anuar Mahmud Hall, University Kebangsaan Malaysia.

Tezel, G. & Buyukyildiz, M. 2016. Monthly evaporation forecasting using artificial neural networks and support vector machines. Theoretical and Applied Climatology 124(1-2): 69-80.

Wei, C.C. 2015. Comparing lazy and eager learning models for water level forecasting in river-reservoir basins of inundation regions. Environmental Modelling & Software 63(1): 137-155.

Xie, H., Tang, H. & Liao, Y.H. 2009. Time series prediction based on NARX neural networks: An advanced approach. 2009 International Conference on Machine Learning and Cybernetics. pp. 1275-1279.

Zime, S. 2014. Africa economic growth forecasting research based on Artificial Neural Network Model: Case study of Benin. International Journal of Engineering Research & Technology (IJERT) 3(11): 1644-1651.

^*Corresponding author; email: wia@umt.edu.my