台灣每年都會發生很多地震，其中的破壞性地震往往會帶來重大的損失，而能有效減少這些地震災害的方法為地震預警系統，能夠在這些強震波發生之前提供警告，來減少地震災害。地震預警系統又可以分成兩種類型，一種是區域型預警，另一種則為現地型預警，區域型預警系統會收集震央附近測站的資訊，如果這些波的資訊顯示出可能會有強震波的到來，系統就會發警報給較遠的地方，但面臨到的問題是，近震央的區域無法及時收到警報，會形成一個預警盲區，等到警報發出的時候，地震早就已經搖完了。所以為了解決預警盲區的問題，才有了現地型預警系統，能夠為近震央區域提供預警，希望每一個地震測站收到地震波的很短的時間內，就可以預測出將來會不會發生劇烈地搖晃，實際上會使用初始P波信號來預測最大的地面運動，傳統方法是學者使用直覺或經驗選擇的某些標準來進行預測。但是，這些標準很難選擇，而且預測的準確性也很容易會受到這些標準的影響。本文研究了基於神經網路的方法，在P波到達地震測站後及早預測地震的最大地面運動。建立模型以使用初始P波加速度信號的幾秒鐘時間窗口進行預測。該模型由台灣1991年至2019年採集的地震波進行訓練，並透過2020年和2021年的事件進行評估。從評估結果來看，所提出的方案在準確性和平均預警時間方面皆明顯優於傳統基於閾值的方法。

On-site earthquake early warning is a challenging problem since the limited time and information collected before the warning determination. A potential solution to prevent severe disasters is to predict the greatest ground motion using the initial P-wave signal and provide warnings before the hit of the shaking. In practice, the accuracy of the prediction is the most critical issue for earthquake early warning systems. Traditional methods use certain criteria selected by intuition or experience to make the prediction. However, the thresholds for the criteria are difficult to select and may significantly affect the accuracy. This paper investigates the methods based on artificial intelligence to predict the greatest ground motion of earthquakes at the early stage when the P-wave arrives at the seismograph stations. A neural network model is built to make the predictions using a small window of the initial P-wave acceleration signal. The model is trained by the seismic waves collected from 1991 to 2019 in Taiwan and evaluated by events in 2020 and 2021. From the evaluations, the proposed scheme significantly outperforms the threshold-based method in terms of the accuracy and the average leading time.

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