FORECASTING SEA LEVEL CHANGES USING HYBRID ARIMA-RADIAL BASIS FUNCTION NEURAL NETWORK METHODS
Abstract
Understanding sea level variability is crucial for ensuring the safety of tourists, particularly in marine tourism areas like Marina Ancol Beach in North Jakarta. Climate change has led to rising sea levels, significantly impacting coastal regions. Accurate predictions of sea level are essential for anticipating tidal flooding, which occurs when seawater inundates these areas. Short-term sea level fluctuations are influenced by both linear tidal patterns and nonlinear local effects, making accurate forecasting challenging when using a single modeling approach. This study proposes a hybrid forecasting method that combines the Autoregressive Integrated Moving Average (ARIMA) model to capture linear temporal structures and a Radial Basis Function Neural Network (RBFNN) to model nonlinear patterns present in the residuals. Hourly sea level data consisting of 17,520 observations collected from January 2021 to December 2022 were analyzed. The proposed hybrid ARIMA–RBFNN model achieved a Mean Absolute Percentage Error (MAPE) of 2.74%, slightly outperforming the ARIMA model, which yielded a MAPE of 2.76%. The model provides accurate 24-hour sea level forecasts for Marina Ancol Beach, offering timely information that can support local authorities in anticipating and mitigating tidal flooding events.
Downloads
References
IPCC, “CLIMATE CHANGE 2021: THE PHYSICAL SCIENCE BASIS,” in Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, 2021. [Online]. Available: www.ipcc.ch
D. Khojasteh, W. Glamore, V. Heimhuber, and S. Felder, “SEA LEVEL RISE IMPACTS ON ESTUARINE DYNAMICS: A REVIEW,” Science of the Total Environment, vol. 780, Aug. 2021, doi: https://doi.org/10.1016/j.scitotenv.2021.146470
Dinas Sumber Daya Air, “PINTU AIR,” DSDA Jakarta.
S. Liu et al., “WARNING WATER LEVEL DETERMINATION AND ITS SPATIAL DISTRIBUTION IN COASTAL AREAS OF CHINA,” Natural Hazards and Earth System Sciences, vol. 23, pp. 127–138, Jan. 2023, doi: https://doi.org/10.5194/nhess-23-127-2023
R. J. Hyndman and G. Athanasopoulos, FORECASTING: PRINCIPLES AND PRACTICE , 2nd ed. Melbourne: OTexts, 2018, doi: https://doi.org/10.32614/CRAN.package.fpp2
I. Syahzaqi, Sediono, M. K. Dyaksa, A. T. Vionita, and A. N. Ghasani, “PREDICTION OF AVERAGE TEMPERATURE IN BANYUWANGI REGENCY USING SARIMA,” Barekeng, vol. 19, no. 3, pp. 2207–2218, 2025, doi: https://doi.org/10.30598/barekengvol19iss3pp2207-2218
W. A. Pratiwi, A. F. Rizki, K. A. Notodiputro, Y. Angraini, and L. N. A. Mualifah, “THE COMPARISON OF ARIMA AND RNN FOR FORECASTING GOLD FUTURES CLOSING PRICES,” Barekeng, vol. 19, no. 1, pp. 397–406, 2025, doi: https://doi.org/10.30598/barekengvol19iss1pp397-406
A. Musbikhin, E. Sediyono, and C. E. Widodo, “MODEL SEA LEVELS PREDICTION WITH ARIMA FOR COASTAL AREA IN SEMARANG,” Proceedings of the International Conference on Innovation in Science and Technology (ICIST 2020), 2021, doi: 10.2991/aer.k.211129.001.
P. K. Srivastava, T. Islam, S. K. Singh, G. P. Petropoulos, M. Gupta, and Q. Dai, “FORECASTING ARABIAN SEA LEVEL RISE USING EXPONENTIAL SMOOTHING STATE SPACE MODELS AND ARIMA FROM TOPEX AND SASON SATELLITE RADAR ALTIMETER DATA,” Meteorological Applications, vol. 23, pp. 633–639, 2016, doi: https://doi.org/10.1002/met.1585
A. Elzwayie, A. El-shafie, Z. M. Yaseen, H. A. Afan, and M. F. Allawi, “RBFNN-BASED MODEL FOR HEAVY METAL PREDICTION FOR DIFFERENT CLIMATIC AND POLLUTION CONDITIONS,” Neural Comput Appl, vol. 28, no. 8, pp. 1991–2003, 2017, doi: https://doi.org/10.1007/s00521-015-2174-7
W. Reza, F. C. H. Buan, and S. N. Abdussamad, “COMPARISON OF RADIAL BASIS FUNCTION NEURAL NETWORKS (RBFNN) AND AUTOREGRESSIVE MOVING AVERAGE (ARMA) ALGORITHMS ON INFLATION RATE PREDICTION MODELS IN BATAM CITY,” Jurnal Info Sains : Informatika dan Sains, vol. 14, no. 02, p. 2024, doi: https://doi.org/10.58471/infosains.v14i02
A. Setiawan, “PREDIKSI TINGGI MUKA AIR MENGGUNAKAN SUPPORT VECTOR MACHINE BERBASIS PARTICLE SWARM OPTIMIZATION,” Jurnal Ilmiah Fakultas Technologia, vol. 7, no. 2, 2016, doi: https://doi.org/10.31602/tji.v7i2.616
A. A. Alsuwaylimi, “COMPARISON OF ARIMA, ANN AND HYBRID ARIMA-ANN MODELS FOR TIME SERIES FORECASTING,” Information Sciences Letters, vol. 12, no. 2, pp. 1003–1016, Feb. 2023, doi: https://doi.org/10.18576/isl/120238
A. P. Baluk, H. Yasin, and Sugito, “PERAMALAN TINGGI GELOMBANG LAUT DENGAN METODE VECTOR AUTOREGRESSIVE-RADIAL BASIS FUNCTION NETWORK (VAR-RBFN),” J Statistika, vol. 13, no. 1, pp. 39–46, 2020, doi: https://doi.org/10.36456/jstat.vol13.no2.a3270.
A. A. Soebroto, I. Cholissodin, R. C. Wihandika, M. T. Frestantiya, and Z. El Arief, “PREDIKSI TINGGI MUKA AIR (TMA) UNTUK DETEKSI DINI BENCANA BANJIR MENGGUNAKAN SVR-TVIWPSO,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 2, p. 79, Jul. 2015, doi: https://doi.org/10.25126/jtiik.201522126
B. L. Bowerman and R. O’Connel, FORECASTING AND TIME SERIES: AN APPLIED APPROACH, 3rd ed. California: Duxburry Press, 1993.
J. D. Cryer and K. S. Chan, TIME SERIES ANALYSIS WITH APPLICATIONS IN R. Springer, 2008, doi: https://doi.org/10.1007/978-0-387-75959-3
K. Ramasubramanian and A. Singh, MACHINE LEARNING USING R . New Delhi: Apress, 2017, doi: https://doi.org/10.1007/978-1-4842-2334-5
G. E. P. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, TIME SERIES ANALYSIS: FORECASTING AND CONTROL. New Jersey: John Wiley and Sons Inc., 2015.
V. Wahyuningrum, “PENERAPAN RADIAL BASIS FUNCTION NEURAL NETWORK DALAM PENGKLASIFIKASIAN DAERAH TERTINGGAL DI INDONESIA,” Jurnal Aplikasi Statistika & Komputasi Statistik, vol. 12, no. 1, 2020, doi: https://doi.org/10.34123/jurnalasks.v12i1.250.
G. P. Zhang, “TIME SERIES FORECASTING USING A HYBRID ARIMA AND NEURAL NETWORK MODEL,” Neurocomputing, vol. 50, pp. 159–175, 2003, doi: https://doi.org/10.1016/S0925-2312(01)00702-0.
Y. Liu, C. Yang, K. Huang, and W. GUI, “NON-FERROUS METALS PRICE FORECASTING BASED ON VARIATIONAL MODE DECOMPOSITION AND LSTM NETWORK,” Knowl Based Syst, vol. 188, p. 105006, 2020, doi: https://doi.org/10.1016/j.knosys.2019.105006.
Copyright (c) 2026 Soehardjoepri Djoepri, Ulil Azmi, Prilyandari Dina Saputri, Moch. Taufik Hakiki, Denisha A. E. Ananda, Roslinazairimah Zakaria
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this Journal agree to the following terms:
- Author retain copyright and grant the journal right of first publication with the work simultaneously licensed under a creative commons attribution license that allow others to share the work within an acknowledgement of the work’s authorship and initial publication of this journal.
- Authors are able to enter into separate, additional contractual arrangement for the non-exclusive distribution of the journal’s published version of the work (e.g. acknowledgement of its initial publication in this journal).
- Authors are permitted and encouraged to post their work online (e.g. in institutional repositories or on their websites) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published works.
1.gif)
