Comparison of ARIMA and LSTM Methods in Predicting Jakarta Sea Level Comparison of ARIMA and LSTM Methods in Predicting Jakarta Sea Level
Abstract
As a coastal city, Jakarta faces enormous risks from sea level rise brought on by climate change, and it is critical to create efficient plans to anticipate and minimize any potential negative effects. Predictive modeling is essential in addressing this challenge in order to anticipate and mitigate any potential negative effects of sea level rise. Therefore, research was conducted with the aim of comparing the performance of two prediction methods, namely Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM). Sea level was predicted using both techniques up to the end of 2023. Performance indicators, including Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE), were employed to assess the quality of both prediction models. The result shows that the ARIMA (1,1,4) model is more effective in predicting sea level than the LSTM. The MAE, MAPE, and RMSE values for ARIMA (1,1,4) are 7.19, 4.86%, and 10.35, respectively. In the meantime, the sea level in Jakarta is predicted to remain reasonably steady, according to the forecasted findings from both models. This study is expected to make a significant contribution to understanding and mitigating the potential impacts of sea level rise in Jakarta as a result of climate change.
References
Balogun, A.L., & Adebisi, N. (2021). Sea level prediction using ARIMA, SVR and LSTM neural network: assessing the impact of ensemble Ocean-Atmospheric processes on models’ accuracy. Geomatics, Natural Hazards and Risk, 12(1), 653–674. https://doi.org/10.1080/19475705.2021.1887372
Bennett, W.G., Karunarathna, H., Xuan, Y., Kusuma, M.S.B., Farid, M., Kuntoro, A. A., Rahayu, H.P., Kombaitan, B., Septiadi, D., Kesuma, T.N.A., Haigh, R., & Amaratunga, D. (2023). Modelling compound flooding: a case study from Jakarta, Indonesia. Natural Hazards, 118(1), 277–305. https://doi.org/10.1007/s11069-023-06001-1
Dobre, I., & Alexandru, A.A. (2008). Modelling unemployment rate using Box-jenkins procedure. Journal of Applied Quantitative Methods, 3(Quantitative Models), 156–166.
Faishol, M.A., Endroyono, E., & Irfansyah, A. N. (2020). Predict Urban Air Pollution in Surabaya Using Recurrent Neural Network – Long Short Term Memory. JUTI: Jurnal Ilmiah Teknologi Informasi, 18(2), 102. https://doi.org/10.12962/j24068535.v18i2.a988
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. In Adaptive Computatuion and Machine Learning series. Massachusetts Institute of Technology. https://lccn.loc.gov/2016022992
Gu, J., Liang, L., Song, H., Kong, Y., Ma, R., Hou, Y., Zhao, J., Liu, J., He, N., & Zhang, Y. (2019). A method for hand-foot-mouth disease prediction using GeoDetector and LSTM model in Guangxi, China. Scientific Reports, 9(1), 17928. https://doi.org/10.1038/s41598-019-54495-2
Gülmez, B. (2023). Stock price prediction with optimized deep LSTM network with artificial rabbits optimization algorithm. Expert Systems with Applications, 227(January), 120346. https://doi.org/10.1016/j.eswa.2023.120346
Hansun, S., & Young, J.C. (2021). Predicting LQ45 financial sector indices using RNN-LSTM. Journal of Big Data, 8(1). https://doi.org/10.1186/s40537-021-00495-x
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Katambire, V.N., Musabe, R., Uwitonze, A., & Mukanyiligira, D. (2023). Forecasting the Traffic Flow by Using ARIMA and LSTM Models: Case of Muhima Junction. Forecasting, 5(4), 616–628. https://doi.org/10.3390/forecast5040034
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Leontinus, G. (2022). Program dalam Pelaksanaan Tujuan Pembangunan Berkelanjutan ( SDGs) Dalam Hal Masalah Perubahan Iklim di Indonesia. Jurnal Samudra Geografi, 5(1), 43–52. https://doi.org/10.33059/jsg.v5i1.4652
Makridakis, S. 1992. Metode dan Aplikasi Peramalan (terjemahan). Jakarta: Erlangga.
Paliari, I., Karanikola, A., & Kotsiantis, S. (2021). A comparison of the optimized LSTM, XGBOOST and ARIMA in Time Series forecasting. 2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA), 1–7. https://doi.org/10.1109/IISA52424.2021.9555520
Pierre, A.A., Akim, S.A., Semenyo, A.K., & Babiga, B. (2023). Peak Electrical Energy Consumption Prediction by ARIMA, LSTM, GRU, ARIMA-LSTM and ARIMA-GRU Approaches. Energies, 16(12). https://doi.org/10.3390/en16124739
Pramesti, D.D., Novitasari, D.C.R., Setiawan, F., & Khaulasari, H. (2022). Long-Short Term Memory (Lstm) for Predicting Velocity and Direction Sea Surface Current on Bali Strait. BAREKENG: Jurnal Ilmu Matematika Dan Terapan, 16(2), 451–462. https://doi.org/10.30598/barekengvol16iss2pp451-462
Ramadhan, A.W., Adytia, D., Saepudin, D., Husrin, S., & Adiwijaya, A. (2021). Forecasting of Sea Level Time Series using RNN and LSTM Case Study in Sunda Strait. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, 12(3), 130. https://doi.org/10.24843/lkjiti.2021.v12.i03.p01
Sugiyono. (2014). Metodologi Penelitian Kuantitatif, Kualitatif dan R & D. Alfabeta.
Sumartapraja, A.R., & Christianti, D.W. (2022). The Right to Water in Jakarta: Limitation in a Sinking City. Padjadjaran Jurnal Ilmu Hukum, 9(1), 67–88. https://doi.org/10.22304/pjih.v9n1.a3
Sun, Q., Wan, J., & Liu, S. (2020). Estimation of Sea Level Variability in the China Sea and Its Vicinity Using the SARIMA and LSTM Models. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 3317–3326. https://doi.org/10.1109/JSTARS.2020.2997817
Syahram, E. F., Effendy, M. M., & Setyawan, N. (2021). Sun Position Forecasting Menggunakan Metode RNN – LSTM Sebagai Referensi Pengendalian Daya Solar Cell. Jurnal JEETech, 2(2), 65–77. https://doi.org/10.48056/jeetech.v2i2.169
Widodo, A. (2017). Analyzing Indonesia’s NCICD Project to Stop the Capital City Sinking. Otoritas : Jurnal Ilmu Pemerintahan, 7(2), 54–66. https://doi.org/10.26618/ojip.v7i2.769
Yamak, P.T., Yujian, L., & Gadosey, P.K. (2019). A comparison between ARIMA, LSTM, and GRU for time series forecasting. ACM International Conference Proceeding Series, 49–55. https://doi.org/10.1145/3377713.3377722
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