Significant Wave Height Forecasting using Long-Short Term Memory (LSTM) in Seribu Island Waters

https://doi.org/10.22146/ijccs.95044

Husnul Khatimah(1*), Indra Jaya(2), Agus Saleh Atmadipoera(3)

(1) IPB University
(2) IPB University
(3) IPB University
(*) Corresponding Author

Abstract


Wind waves are natural phenomena primarily generated by the wind. Information about wave height and period is highly crucial in various marine fields such as coastal engineering, fisheries, and maritime transportation. However, accurately predicting wave height remains a challenge due to the stochastic nature of ocean waves themselves. Several approaches to predicting wave height have been developed, including numerical models and machine learning methods, such as the Long-Short Term Memory (LSTM) algorithm, which has currently garnered significant attention from researchers. The objective of this research is to develop a forecast model for wind wave height using the LSTM algorithm in Seibu Island Waters, DKI Jakarta. The ERA5 dataset comprises zonal and meridional wind components and significant wave height, along with wind measurement data using the Automatic Weather System (AWS) instrument, are used to train and test to train and test the LSTM model. The research results show that the LSTM model can predict significant wave height effectively. Predictions using the ERA5 significant height dataset are observed to be closer to field data, with RMSE, MAE, and MAPE values of 0.1535 m, 0.1181 m, and 37.11% respectively. Thus, the model evaluation results indicate good performance, with relatively low RMSE and MAE values, and a good MAPE value. The highest accuracy in significant wave height prediction is found for forecasts one week (7 days) ahead


Keywords


Deep Learning, Forecast, LSTM, Ocean Wave, Significant Wave Height

Full Text:

PDF


References

B. Utoyo, "Geografi Membuka Cakrawala Dunia," PT. Setia Purna Inves, Bandung, 2007. [2] S. Fan, N. Xiao, and S. Dong, “A novel model to predict significant wave height based on long short-term memory network,” Ocean Eng., vol. 205, Jun. 2020, doi: 10.1016/j.oceaneng.2020.107298. [3] I.R. Young and A. Ribal, "Multiplatform evaluation of global trends in wind speed and wave height," Science, vol. 364, pp. 548–552, 2019, doi:10.1126/science.aav9527 [4] A.W. Pramita, D.N. Sugianto, I.B. Prasetyawan, R. Kurniawan, A.S. Praja, "Pola Tinggi Gelombang di Laut Jawa Menggunakan Model Wavewatch-III," Jurnal Meteorologi dan Geofisika, vol. 21, no. 1, pp. 21-28, 2020. [5] P. Purwanto, R. Tristanto, G. Handoyo, M. Trenggono, and A. A. D. Suryoputro, "Analisis Peramalan dan Periode Ulang Gelombang di Perairan Bagian Timur Pulau Lirang, Maluku Barat Daya," Indonesian Journal of Oceanography, vol. 2, no. 1, pp. 80-89, Mar. 2020. [Online]. Available: https://doi.org/10.14710/ijoce.v2i1.7481 [6] H. Yoon, S.C. Jun, Y. Hyun, G.O. Bae, K.K. Lee, "A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer," J. Hydrol., vol. 396, pp. 128–138, 2011. [Online]. Available: https://doi.org/10.1016/j.jhydrol.2010.11.002 [7] H.A.A. Guner, Y. Yuksel and E.O. Cevik, "Estimation of wave parameters based on nearshore wind-wave correlations," Ocean Eng., vol. 63, pp. 52–62, 2013. https://doi.org/10.1016/j.oceaneng.2013.01.023 [8] W. Wang, R. Tang, C. Li, P. Liu, L. Luo, "A BP neural network model optimized by Mind Evolutionary Algorithm for predicting the ocean wave heights," Ocean Eng., vol. 162, pp. 98–107, 2018. [Online]. Available: https://doi.org/10.1016/j.oceaneng.2018.04.039. [9] A.B. Bottcher, B.J. Whiteley, A.I. James, J.G. Hiscock, "Watershed Assessment Model (WAM): Model Use, Calibration, and Validation," Trans. ASABE, vol. 55, pp. 1367–1383, 2012. [Online]. Available: doi: 10.13031/2013.42248 [10] H.L. Tolman, "User manual and system documentation of WAVEWATCH III TM version 3.14," Technical note, MMAB Contrib., no. 276, pp. 220, 2009. [11] L. Mentaschi, G. Besio, F. Cassola, A. Mazzino, "Performance evaluation of Wavewatch III in the Mediterranean Sea," Ocean Model., vol. 90, pp. 82–94, 2015. https://doi.org/10.1016/j.ocemod.2015.04.003 [12] A. Akpınar, G.P. van Vledder, M.I. Kömürcü, M. Özger, "Evaluation of the numerical wave model (SWAN) for wave simulation in the Black Sea," Cont. Shelf Res., vol. 50, pp. 80–99, 2012. [Online]. Available: https://doi.org/10.1016/j.csr.2012.09.012 [13] B. Liang, H. Gao and Z. Shao, "Characteristics of global waves based on the third-generation wave model SWAN," Mar. Struct., vol. 64, pp. 35–53, 2019. [Online]. Available: https://doi.org/10.1016/j.marstruc.2018.10.011 [14] N. Rahmadani, B. Darma Setiawan, and S. Adinugroho, "Prediksi Ketinggian Gelombang Laut Menggunakan Metode Jaringan Saraf Tiruan Backpropagation," Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 3, no. 7, pp 6517-6525, 2019.[Online]. Available: http://j-ptiik.ub.ac.id. [15] V. Juliani, D. Aditya and Adiwijaya, "Wave Height Prediction based on Wind Information by using General Regression Neural Network, Study Case in Jakarta Bay," IEEE explorer, doi: 10.1109/ICoICT49345.2020.9166305, 2020. [16] A. Huang, B. Vega-Westhoff and R.L. Sriver, "Analyzing El Niño–Southern Oscillation Predictability Using Long-Short-Term-Memory Models," Earth Sp Sci., vol. 6, no. 2, pp. 212–221, 2019. [Online]. Available: https://doi.org/10.1029/2018EA000423. [17] M. Rizki, S. Basuki and Y. Azhar, "Implementasi Deep Learning Menggunakan Arsitektur Long-Short Term Memory Untuk Prediksi Curah Hujan Kota Malang," Repositor, vol. 2, no. 3, pp. 331–338, 2020. [18] S.B. Park, S.Y. Shin, K.H. Jung, B.G. Lee, "Prediction of Significant Wave Height in Korea Strait Using Machine Learning," J Ocean Eng Technol., vol. 35, no. 5, pp. 336–346, 2021. [Online]. Available: doi: 10.26748/ksoe.2021.021. [19] F.C. Minuzzi and L. Farina, "A deep Learning Approach to Predict Significant Wave Height using Long Short-Term Memory," Ocean Modelling, vol. 181, 2023. [Online]. Available: https://doi.org/10.1016/j.ocemod.2022.102151. [20] P. Hao, S. Li and Y. Gao, "Significant Wave Height Prediction Based on Deep Learning in The South China Sea," Front. Mar. Sci., vol. 9, pp. 1113788, 2023. [Online]. Available: doi: 10.3389/fmars.2022.1113788. [21] T. Song, J. Jiang, W. Li, D. Xu, "A Deep Learning Method With Merged LSTM Neural Networks for SSHA Prediction," IEEE Journal Of Selected Topics In Applied Earth Observations And Remote Sensing, vol. 13, pp. 2853–2860, 2020. [22] X. Zhang, Y. Li, S. Gao, P. Gen, "Ocean Wave Height Series Prediction with Numerical Long Short-Term Memory," Marine Science and Engineering, vol. 9, no. 5, pp. 514, 2021. [Online]. Available: https://doi.org/10.3390/jmse9050514. [23] A. Ahmed, J.J. Jui, M.S. Al-Musaylh, N. Raj, R. Saha, R.C. Deo, S.K. Saha, "Hybrid Deep Learning Model for Wave Height Prediction in Australia’s Wave Energy Region," Applied Soft Computing Journal, vol. 150, pp. 111003, 2023. [Online]. Available: https://doi.org/10.1016/j.asoc.2023.111003. [24] H. Hersbach, B. Bell, P. Berrisford, S. Hirahara, A. Horányi, J. Muñoz-Sabater, J. Nicolas, C. Peubey, R. Radu, D. Schepers, et al., "The ERA5 global reanalysis," Q J R Meteorol. Soc., vol. 146, no. 730, pp. 1999–2049, 2020. [Online]. Available: doi: 10.1002/qj.3803. [25] Y.K. Jain and S.K. Bhandare, "Min max normalization based data perturbation method for privacy protection," International Journal of Computer & Communication Technology (IJCCT), vol. 2, no. 8, pp. 45–50, 2011. [Online]. Available: 10.47893/ijcct.2013.1201 [26] S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Comput., vol. 9, pp. 1735–1780, 1997. [Online]. Available: https://doi.org/10.1162/neco.1997.9.8.1735. [27] N. K. Manaswi, "Deep Learning with Applications Using Python," Apress, 2018. [28] I. Delgado and M. Fahim, "Wind turbine data analysis and LSTM-based prediction in SCADA system," Energies, vol. 14, no. 1, pp. 125-140, 2021. [Online]. Available: https://doi.org/10.3390/en14010125. [29] D. P. Kingma and J. Ba, "Adam: A Method for Stochastic Optimization," ICLR (International Conference for Learning Representations). [Online]. Available: http://arxiv.org/abs/1412.6980 [30] D. Z. Haq, DCR. Novitasari, A. Hamid, N. Ulinnuha, Arnita, Y. Farida, D. Nugraheni, R. Nariswari, Ilham, H. Rohayani, and others, "Long Short-Term Memory algorithm for rainfall prediction based on El-Nino and IOD data," Procedia Comput. Sci., vol. 179, pp. 829–837, 2021. doi: 10.1016/j.procs.2021.01.071. [Online]. Available: https://doi.org/10.1016/j.procs.2021.01.071. [31] Y. J. Wai, Zulkarnain, S. Irwan, and LK. Chuan, "Fixed point implementation of Tiny-Yolo-v2 using opeCL on FPGA," Intenational Journal of Advanced Computer Science and Applications, vol. 9, no. 10, pp. 506-512, 2018. [Online]. Available: 10.14569/IJACSA.2018.091062 [32] Y. Bai, Y. Guo, Q. Zhang, B. Cao, and B. Zhang, "Multi-network fusion algorithm with transfer learning for green cucumber segmentation and recognition under complex natural environment," Computers and Electronics in Agriculture, vol. 194, pp. 106789, 2022. doi.org/10.1016/j.compag.2022.106789. [Online]. Available: https://doi.org/10.1016/j.compag.2022.106789. [33] M. M. Puspham and F. Enigo, "Forecasting Significant Wave Height using RNN-LSTM Models," Proceedings of the International Conference on Intelligent Computing and Control Systems, pp. 1141-1146, 2020. ISBN: 978-1-7281-4876-2. [Online]. Available: 10.1109/ICICCS48265.2020.9121040 [34] P. J. Webster, "Dynamics of the Tropical Atmosphere and Oceans (Advancing Weather and Climate Science): 1st Edition," Georgia Institute of Technology, 2020. [35] T. Qu, Y. Dua, J. Strachan, G. Meyers, and J. Slingo, "Sea surface temperature and its variability in the Indonesian region," Oceano, vol. 18, pp. 50-6, 2005. [Online]. Available: 10.5670/oceanog.2005.05 [36] I. A.H Aswad, H. D Armono, S. Rahmawati, A. Rodlwan and R. M. Arifieanto, "Modeling Wave Height for Wave Energy Study in the Western Waters of Lampung Province," J. Ilm. Teknol. Marit., vol. 15, no. 2, pp. 75-84, 2021. [Online]. Available: 0.29122/jurnalwave.v15i2.4958



DOI: https://doi.org/10.22146/ijccs.95044

Article Metrics

Abstract views : 639 | views : 573

Refbacks

  • There are currently no refbacks.




Copyright (c) 2024 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.



Copyright of :
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
ISSN 1978-1520 (print); ISSN 2460-7258 (online)
is a scientific journal the results of Computing
and Cybernetics Systems
A publication of IndoCEISS.
Gedung S1 Ruang 416 FMIPA UGM, Sekip Utara, Yogyakarta 55281
Fax: +62274 555133
email:ijccs.mipa@ugm.ac.id | http://jurnal.ugm.ac.id/ijccs



View My Stats1
View My Stats2