Forecasting Sea Level Rise Using Hybrid ARIMA-Radial Basis Function Neural Network (RBFNN) Methods

DOI: https://doi.org/10.30598/barekengvol20iss3pp2045-2062
Keywords: ARIMA, Climate Change, Marina Ancol Beach, Radial Basis Function Neural Network, Sea Level

Abstract

Understanding sea level fluctuations 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. This study focuses on predicting sea levels at Marina Ancol Beach using a hybrid approach that combines ARIMA for initial forecasting and RBFNN for modeling the residuals. We analyzed a dataset comprising 17,520 observations collected from January 2021 to December 2022. Our findings indicate that the hybrid ARIMA-RBFNN approach yields a Mean Absolute Percentage Error (MAPE) of 2.74%, compared to 2.76% for the ARIMA model alone. The results provide a 24-hour sea level prediction for Marina Ancol, offering timely and accurate data that can aid DKI Jakarta's government in preparing for and mitigating future tidal flooding events.

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Published
2026-04-08
How to Cite
[1]
S. Djoepri, U. Azmi, P. D. Saputri, M. T. Hakiki, D. A. E. Ananda, and R. Zakaria, “Forecasting Sea Level Rise Using Hybrid ARIMA-Radial Basis Function Neural Network (RBFNN) Methods”, BAREKENG: J. Math. & App., vol. 20, no. 3, pp. 2045-2062, Apr. 2026.
Issue
Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Section
Articles

Copyright (c) 2026 Soehardjoepri Djoepri, Ulil Azmi, Prilyandari Dina Saputri, Moch. Taufik Hakiki, Denisha A. E. Ananda, Roslinazairimah Zakaria

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