LONG SHORT TERM MEMORY APPROACH FOR SHORELINE CHANGE PREDICTION ON ERETAN BEACH
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
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Copyright (c) 2024 Iryanto Iryanto, Ari Satrio, Ahamd Lubis Ghozali, Eka Ismantohadi, ZK Abdurahman Baizal, Putu Harry Gunawan
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