LONG SHORT TERM MEMORY APPROACH FOR SHORELINE CHANGE PREDICTION ON ERETAN BEACH

  • Iryanto Iryanto (1) Politeknik Negeri Indramayu
  • Ari Satrio (2) Politeknik Negeri Indramayu
  • Ahmad Lubis Ghozali (3) Politeknik Negeri Indramayu
  • Eka Ismantohadi (4) Politeknik Negeri Indramayu
  • ZK Abdurahman Baizal (5) Telkom University
  • Putu Harry Gunawan (6*) Telkom University

  • (*) Corresponding Author
Keywords: eretan shoreline change, long short-term memory, shoreline change, shoreline change prediction

Abstract

Eretan Beach is one of the beaches in Indramayu and has a reasonably severe abrasion rate from year to year. The Eretan coastline always experiences significant changes due to erosion every year. Therefore, it is necessary to study changes in the coastline at Eretan beach. This study obtained coastline data from the Google Earth engine using CoastSat, a python-based open-source toolkit, from 1992 – 2022. The open-source geographic information system software used to process the data is the Quantum Geographic Information System. This study aims to analyze the Long Short-term Memory (LSTM) algorithm in predicting shoreline changes at Eretan Beach. The eight optimizer functions in the LSTM are used with nine different scenarios to analyze the algorithm's performance. The results of this study show that RMSProp has the best performance compared to other optimizers. The RMSE and MAPE values on the RMSProp are 35.06258 and 2.2923 on the training data and 9.2457 and 1.06786 on the test data. In addition, from the predictions for the next ten years at transect point 251, it was found that there would be an increase in the coastline.

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References

Kusnanto, Y. Setiawan, and I. W. Nurjaya, “Coastline Changes in Indramayu Regency Between 1989-2019,” J. Pengelolaan Sumberd. Alam dan Lingkung. (Journal Nat. Resour. Environ. Manag., vol. 12, no. 3, 2022.

S. H. Nur and E. Hilmi, “The correlation between mangrove ecosystem with shoreline change in Indramayu coast,” in IOP Conference Series: Earth and Environmental Science, 2021, vol. 819, no. 1.

F. Kasim, “Laju Perubahan Garis Pantai Menggunakan Modifikasi Teknik Single Transect (ST) dan Metode End Point Rate (EPR): Studi Kasus Pantai Sebelah Utara Indramayu-Jawa barat,” J. Ilm. Agropolitan, vol. 3, no. September, 2010.

F. Calkoen, A. Luijendijk, C. R. Rivero, E. Kras, and F. Baart, “Traditional vs. Machine-learning methods for forecasting sandy shoreline evolution using historic satellite-derived shorelines,” Remote Sens., vol. 13, no. 5, 2021.

A. L. Balogun and N. Adebisi, “Sea level prediction using ARIMA, SVR and LSTM neural network: assessing the impact of ensemble Ocean-Atmospheric processes on models’ accuracy,” Geomatics, Nat. Hazards Risk, vol. 12, no. 1, 2021.

C. Yin et al., “Advanced Machine Learning Techniques for Predicting Nha Trang Shorelines,” IEEE Access, vol. 9, 2021.

D. W. Tyas, F. S. C. Rosaji, M. A. Marfai, and N. Khakhim, “Spatial modeling for silvofishery and greenbelt to reduce the risk of sea level rise in indramayu coastal area, West Java-Indonesia,” in Proceedings of the Pakistan Academy of Sciences: Part B, vol. 56, no. 1, 2019.

X. H. Le, H. V. Ho, G. Lee, and S. Jung, “Application of Long Short-Term Memory (LSTM) neural network for flood forecasting,” Water (Switzerland), vol. 11, no. 7, 2019.

J. Brownlee, “How to Choose Loss Functions When Training Deep Learning Neural Networks,” Mach. Learn. Mastery, 2019.

Iryanto, P. H. Gunawan, A. Satrio, Z. A. Baizal, A. L. Ghozali, and E. Ismantohadi, “Shoreline Change Forecasting on Eretan Beach using Long Short Term Memory,” in ICOIACT 2022 - 5th International Conference on Information and Communications Technology: A New Way to Make AI Useful for Everyone in the New Normal Era, Proceeding, 2022.

P. H. Gunawan, Iryanto, A. L. Ghozali, E. Ismantohadi, Z. K. A. Baizal, and A. Satrio, “Data-driven Shoreline Change Forecasting on Eretan Beach Using Random Forest,” in ICACNIS 2022 - 2022 International Conference on Advanced Creative Networks and Intelligent Systems: Blockchain Technology, Intelligent Systems, and the Applications for Human Life, Proceeding, 2022.

K. Vos, K. D. Splinter, M. D. Harley, J. A. Simmons, and I. L. Turner, “CoastSat: A Google Earth Engine-enabled Python toolkit to extract shorelines from publicly available satellite imagery,” Environ. Model. Softw., vol. 122, 2019.

QGIS Project, “QGIS 3.10 Training Manual,” QGIS Proj., 2020.

E. R. Thieler, E. . Himmelstoss, J. . Zichichi, and A. Ergul, “DSAS 4.0 Installation Instructions and User Guide,” U.S. Geological Survey Open-File Report 2008-1278, vol. 3. 2009.

S. Khallaghi and R. G. Pontius, “Area method compared with Transect method to measure shoreline movement,” Geocarto Int., vol. 37, no. 20, 2022.

N. Thankappan, N. Varangalil, T. Kachapally Varghese, and K. Njaliplackil Philipose, “Coastal morphology and beach stability along Thiruvananthapuram, south-west coast of India,” Nat. Hazards, vol. 90, no. 3, 2018.

J. Zhang, P. Wang, R. Yan, and R. X. Gao, “Long short-term memory for machine remaining life prediction,” J. Manuf. Syst., vol. 48, 2018.

A. Moghar and M. Hamiche, “Stock Market Prediction Using LSTM Recurrent Neural Network,” in Procedia Computer Science, 2020, vol. 170.

P. H. Gunawan, D. Munandar, and A. Z. Farabiba, “Long Short-Term Memory Approach for Predicting Air Temperature In Indonesia,” J. Online Inform., vol. 5, no. 2, 2020.

A. Yadav, C. K. Jha, and A. Sharan, “Optimizing LSTM for time series prediction in Indian stock market,” in Procedia Computer Science, 2020, vol. 167.

A. Muslim, A. B. Mutiara, R. Refianti, C. M. Karyati, and G. Setiawan, “Comparison of accuracy between long short-term memory-deep learning and multinomial logistic regression-machine learning in sentiment analysis on twitter,” Int. J. Adv. Comput. Sci. Appl., no. 2, 2020.

M. A. Hashmani, M. Umair, and H. Keiichi, “Wave Parameters Prediction for Wave Energy Converter Site using Long Short-Term Memory,” Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 3, 2022.

M. A. Alsharaiah et al., “Attention-based Long Short Term Memory Model for DNA Damage Prediction in Mammalian Cells,” Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 9, 2022.

D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,” PeerJ Comput. Sci., vol. 7, 2021.

Published
2024-02-01
How to Cite
[1]
I. Iryanto, A. Satrio, A. Ghozali, E. Ismantohadi, Z. Baizal, and P. Gunawan, “LONG SHORT TERM MEMORY APPROACH FOR SHORELINE CHANGE PREDICTION ON ERETAN BEACH”, jitk, vol. 9, no. 2, pp. 227-235, Feb. 2024.
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