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Oktavian, Zulfaa Dwi
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Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Peramalan Tinggi Muka Air Menggunakan Long-Short Term Memory dengan Mekanisme Multi-Head Attention Atmaja, Anugerah Surya; Muzakki, Naufal Fadli; Oktavian, Zulfaa Dwi
Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2024i1.2125

Abstract

This study aims to predict the Ciliwung River water level in DKI Jakarta using an Long-Short Term Memory (LSTM) model with a multi-head attention mechanism. Increasing flood frequency due to climate change necessitates an effective early warning system. Utilizing historical water level data and related meteorological variables, the LSTM model with multi-head attention demonstrated superior performance, with Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Symmetric Mean Absolute Percentage Error (SMAPE) of 31.74, 3.3%, and 3.3%, respectively. Predictions for the next 72 hours indicate safe water levels between 450 cm and 500 cm, suggesting no flooding. In conclusion, the LSTM model with multi-head attention enhances water level forecasting accuracy and serves as a useful flood risk mitigation tool in Jakarta. This research significantly contributes to the development of flood early warning systems and the application of machine learning in disaster mitigation.
Co-Authors Atmaja, Anugerah Surya Muzakki, Naufal Fadli