* Corresponding Author
Hide
Show all
Prediction of Sea Surface Current Velocity and Direction Using LSTM
https://doi.org/10.22146/ijeis.63669 Irkhana Indaka Zulfa(1), Dian Candra Rini Novitasari(2*), Fajar Setiawan(3), Aris Fanani(4), Moh. Hafiyusholeh(5)
(1) Department of Mathematics, Sains and Technology UIN Sunan Ampel Surabaya
(2) Department of Mathematics, Sains and Technology UIN Sunan Ampel Surabaya
(3) Perak Maritime Meteorology Station II, Surabaya
(4) Department of Mathematics, Sains and Technology UIN Sunan Ampel Surabaya
(5) Department of Mathematics, Sains and Technology UIN Sunan Ampel Surabaya
(*) Corresponding Author
Abstract
Labuan Bajo is considered to have an important role as a transportation route for traders and tourists. Therefore, it is necessary to have a further understanding of the condition of the waters in Labuan Bajo, one of them is sea currents. The purpose of this research is to predict sea surface flow velocity and direction using LSTM. There are many prediction methods, one of them is Long short-term memory (LSTM). The fundamental of LSTM is to process information from the previous memory by going through three gates, that is forget gate, input gate, and output gate so the output will be the input in the next process. Based on trials with several parameters namely Hidden Layer, Learning Rate, Batch Size, and Learning rate drop period, it achieved the smallest MAPE values of U and V components of 14.15% and 8.43% with 50 hidden layers, 32 Batch size and 150 Learn rate drop.
Keywords
Full Text:
PDFReferences
[1] D. H. N. Meo, I. N. Sudiarta, and I. K. Suwena, “Analisis Kepuasan Wisatawan Mancanegara Terhadap Tourist Information Centre Di Labuan Bajo, Nusa Tenggara Timur,” Jurnal IPTA (Industri Perjalanan Wisata), 2019. https://ojs.unud.ac.id/index.php/pariwisata/article/view/56070 (accessed Sep. 02, 2020).
[2] G. M. Yogaswara, E. Indrayanti, and H. Setiyono, “Pola Arus Permukaan di Perairan Pulau Tidung, Kepulauan Seribu, Provinsi DKI Jakarta pada Musim Peralihan (Maret-Mei),” Journal of Oceanography, 2016. https://ejournal3.undip.ac.id/index.php/joce/article/view/11448 (accessed Sep. 03, 2020).
[3] D. D. Kartika, D. C. R. Novitasari, and F. Setiawan, “Prediksi Kecepatan Arus Laut Di Perairan Selat Bali Menggunakan Metode Exponential Smoothing Holt-Winters,” MathVisioN, 2020. http://journal.unirow.ac.id/index.php/mv/article/view/137.
[4] Laily Jumhuriyah, Dian C. Rini Novitasari, and Fajar Setiawan, “Prediksi Kecepatan Arus Laut dengan Menggunkan Metode Backpropagation (Studi Kasus: Labuhan Bajo),” 2020. http://jurnalsaintek.uinsby.ac.id/mhs/index.php/algebra/article/view/3.
[5] D. Karmiani, R. Kazi, A. Nambisan, A. Shah, and V. Kamble, “Comparison of Predictive Algorithms: Backpropagation, SVM, LSTM and Kalman Filter for Stock Market,” Amity International Conference on Artificial Intelligence (AICAI), 2019. https://ieeexplore.ieee.org/document/8701258 (accessed Sep. 03, 2020).
[6] S. Siami-Namini and A. S. Namin, “Forecasting Economics and Financial Time Series: ARIMA vs. LSTM,” arXiv preprint arXiv:1803.06386, 2018. https://arxiv.org/abs/1803.06386 (accessed Sep. 03, 2020).
[7] H. Apaydin, H. Feizi, M. T. Sattari, M. S. Colak, S. Shamshirband, and K. W. Chau, “Comparative analysis of recurrent neural network architectures for reservoir inflow forecasting,” Water (Switzerland), 2020. https://www.mdpi.com/2073-4441/12/5/1500 (accessed Dec. 04, 2020).
[8] İ. Kırbaş, A. Sözen, A. D. Tuncer, and F. Ş. Kazancıoğlu, “Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches,” Chaos, Solitons & Fractals, 2020. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7293493/ (accessed Sep. 11, 2020).
[9] S. Hutabarat and S. M. Evans, “Pengantar oseanografi,” 1985. .
[10] M. F. Azis, “Gerak Air di Laut,” Oseana, 2006. .
[11] B. Aydoǧan, B. Ayat, M. N. Öztürk, E. Özkan Çevik, and Y. Yüksel, “Current velocity forecasting in straits with artificial neural networks, a case study: Strait of Istanbul,” Ocean Engineering, 2010. https://doi.org/10.1016/j.oceaneng.2010.01.016 (accessed Sep. 18, 2020).
[12] BMKG, “computing wind direction and speed from u and v.”
[13] Y. Sudriani, I. Ridwansyah, and H. A Rustini, “Long short term memory (LSTM) recurrent neural network (RNN) for discharge level prediction and forecast in Cimandiri river, Indonesia,” IOP Conference Series: Earth and Environmental Science, 2019. https://iopscience.iop.org/article/10.1088/1755-1315/299/1/012037.
[14] S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, 1997. .
[15] J. Zhang, Y. Zhu, X. Zhang, M. Ye, and J. Yang, “Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas,” J. Hydrol., 2018.
[16] N. K. Manaswi, “Deep Learning with Applications Using Python,” Apress, 2018. https://link.springer.com/book/10.1007/978-1-4842-3516-4 (accessed Sep. 03, 2020).
[17] L. Wei, L. Guan, and L. Qu, “Prediction of Sea Surface Temperature in the South China Sea by Artificial Neural Networks,” IEEE Geoscience and Remote Sensing Letters, 2020. https://ieeexplore.ieee.org/document/8777116 (accessed Sep. 03, 2020).
[18] A. R. Isnain, A. Sihabuddin, and Y. Suyanto, “Bidirectional Long Short Term Memory Method and Word2vec Extraction Approach for Hate Speech Detection,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2020. https://jurnal.ugm.ac.id/ijccs/article/view/51743 (accessed Jan. 26, 2021).
[19] S. Ruder, “An overview of gradient descent optimization algorithms,” arXiv preprint arXiv:1609.04747, 2016. https://arxiv.org/abs/1609.04747 (accessed Dec. 20, 2021).
[20] D. P. Kingma and J. L. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014. https://arxiv.org/abs/1412.6980 (accessed Dec. 03, 2020).
[21] Z. Chang, Y. Zhang, and W. Chen, “Effective Adam-optimized LSTM Neural Network for Electricity Price Forecasting,” 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS), 2018. https://ieeexplore.ieee.org/document/8663710.
[22] N. Sakinah, M. Tahir, T. Badriyah, and I. Syarif, “LSTM with Adam Optimization-Powered High Accuracy Preeclampsia Classification,” 2019 International Electronics Symposium (IES), 2019. https://ieeexplore.ieee.org/abstract/document/8901536.
[23] D. Z. Haq et al., “Long Short-Term Memory Algorithm for Rainfall Prediction Based on El-Nino and IOD Data,” Procedia Computer Science, 2020. https://doi.org/10.1016/j.procs.2021.01.071.
[24] G. P. Zhang, “Recurrent Neural networks for time-series forecasting,” arXiv preprint arXiv:1901.00069, 2019. .
[25] H. Yao, X. Tang, H. Wei, G. Zheng, and Z. Li, “Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction,” Proceedings of the AAAI Conference on Artificial Intelligence., 2019. https://arxiv.org/abs/1803.01254.
[26] P. Sugiartawan, R. Pulungan, and A. Kartika, “Prediction by a Hybrid of Wavelet Transform and Long-Short-Term-Memory Neural Network,” International Journal of Advanced Computer Science and Applications, 2017. .
[27] J. S. Armstrong and R. Fildes, “On the selection of error measures for comparisons among forecasting methods,” Journal of Forecasting, 1995. .
[28] D. Emang, M. Shitan, A. N. Abd. Ghani, and K. M. Noor, “Forecasting with Univariate Time Series Models: A Case of Export Demand for Peninsular Malaysia’s Moulding and Chipboard,” Journal of Sustainable Development, 2010. .
[29] Lewis, “Industrial and Business Forecasting Methods: A Practical Guide to Exponential Smoothing and Curve Fitting,” Butterworth Sci., 1982, doi: 10.1002/for.3980010202.
[30] S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” International conference on machine learning, 2016. https://arxiv.org/abs/1502.03167 (accessed Apr. 28, 2021).
[31] I. Kandel and M. Castelli, “The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset,” ICT Express, 2020. https://doi.org/10.1016/j.icte.2020.04.010.
[32] W. W. Rwanda, N. Nikentari, and A. Uperati, “Prediksi Kecepatan Arus Laut Perairan Pulau Bintan Menggunakan Radial Basis Function Neural Network (RBFNN),” 2018. http://repository.umrah.ac.id/174/ (accessed Sep. 03, 2020).
[33] M. M. Najafabadi, F. Villanustre, T. M. Khoshgoftaar, N. Seliya, R. Wald, and E. Muharemagic, “Deep learning applications and challenges in big data analytics,” Journal of Big Data, 2015. https://journalofbigdata.springeropen.com/articles/10.1186/s40537-014-0007-7 (accessed Dec. 17, 2020).
[34] S. Kalyoncu, A. Jamil, E. Karatas, J. Rasheed, and C. Djeddi, “Stock Market Value Prediction using Deep Learning,” Data Science and Applications, 2020. .
[35] M. Semba, R. Lumpkin, I. Kimirei, Y. Shaghude, and N. Nyandwi, “Seasonal and spatial variation of surface current in the Pemba Channel, Tanzania,” PloS one, 2019. https://journals.plos.org/plosone/article/figure?id=10.1371/journal.pone.0210303.g012.
[36] V. H. Saputra, A. Rifai, and Kunarso, “Variabilitas musiman pola arus di perairan surabaya jawa timur,” Journal of Oceanography, 2017. https://ejournal3.undip.ac.id/index.php/joce/article/view/20203.
[37] S. Zallesa and A. Zaelani, “Kajian Arus Permukaan dengan Menggunakan Pendekatan Model Dinamika di Perairan Pulau Gili Terawangan Lombok, Nusa Tenggara Barat,” Jurnal Akuatek, 2020. http://jurnal.unpad.ac.id/akuatek/article/view/31295.
[38] C. M. Simatupang, H. Surbakti, and A. Agussalim, “Analisis Data Arus di Perairan Muara Sungai Banyuasin Provinsi Sumatera Selatan,” Maspari Journal, 2016. https://ejournal.unsri.ac.id/index.php/maspari/article/view/2646 (accessed Apr. 28, 2021).
DOI: https://doi.org/10.22146/ijeis.63669
Article Metrics
Abstract views : 3620
| 
views : 2680
Refbacks
- There are currently no refbacks.
Copyright (c) 2021 IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats1