Tsunami tide prediction in shallow water using recurrent neural networks: model implementation in the Indonesia Tsunami Early Warning System
Willy Dharmawan, Mery Diana, Beti Tuntari, I Made Astawa, Sasono Rahardjo, Hidetaka Nambo
Abstract
Abstract Near-field tides prediction for tsunami detection in the coastal area is a significant problem of the cable-based tsunami meter system in north Sipora, Indonesia. The problem is caused by its shallow water condition and the unavailability of an applicable model or research for tsunami detection in this area. The problem foundation of shallow water area is its ambient noise level-dependent property that requires preprocessing to improve its feature representation. Moreover, because this shallow water is close to the land area, we must consider a model that can accommodate low prediction time for a Tsunami Early Warning System. Therefore, we propose a recurrent neural network (RNN) model because of its reliable performance for time series forecasting. Our report evaluates variants of the RNN model (the vanilla RNN, LSTM and GRU models) in tides prediction and z-score analysis for tsunami identification. The GRU model overwhelms the other two variants in error scores and time processed (training and prediction). It can achieve median error score distribution of $$7.8 \times 10^{-5}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>7.8</mml:mn> <mml:mo>×</mml:mo> <mml:msup> <mml:mn>10</mml:mn> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>5</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> on the L1000-P250 configuration with time prediction under 0.1 s. This lower-time prediction is necessary to ensure the early warning system is going well. Moreover, the GRU model can identify all synthetic tsunami tide spikes (compared with the ground truth result) from magnitude 7.2–8.2 by applying a z-score on the GRU’s prediction.