This study develops a real-time tsunami prediction method using Bi-LSTM, a type of deep learning, which is more accurate than LSTM because Bi-LSTM uses time series data in both directions. The network was constructed by 900 tsunami simulation results with different epicenters and slip distributions, using 10-minute observations at 56 offshore tsunami stations as input data and 11 cities and towns along the coast of Wakayama Prefecture as output data. The hyperparameters were set using Optuna, an automatic optimization framework. The results showed that the correlation coefficients of the maximum tsunami heights and the arrival times of the tsunamis were better than 0.8 and 10 minutes, respectively, at all locations. Furthermore, the applicability of the proposed method to the Showa Tonankai earthquake and the Nankai Trough giant earthquake model was confirmed.
"},{"@language":"ja","@value":"本研究では，深層学習の一種で時系列データを双方向で用いるBi-LSTMを用いて，和歌山県沿岸に到達する津波水位時系列をリアルタイムに予測する手法を開発する．震央やすべり分布，Mwの異なる900ケースの津波解析を実施し，沖合津波観測点56地点における10分間の観測値を入力データ，和歌山県沿岸の11市町への到達津波を出力データとするネットワークを構築し，学習と予測を行った．ハイパーパラメータの設定には自動最適化フレームワークOptunaを用いた．テストデータによる精度評価の結果，全ての地点で最大津波高については相関係数0.8以上，到達時間についてはRMSEが10数分以下の精度で予測可能であった．さらに，昭和東南海地震および南海トラフ巨大地震モデルを対象にした予測を行い，本手法の適用性を確認した．
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