The sudden drop in the quality of an optical signal, called Q-drop, is an important factor for predicting network outages. Herein, we classify sudden drop events into two classes: one the results in a network outage, and one that does not result in a network outage, for the same period of time. Therefore, we build a predictor based on machine learning. The features of the predictor are given by characterizing the Q-drop event based on the optical layer characteristics immediately before the Q-drop event. The predictor is trained for each Q-drop event to adapt to the temporal change of the optical layer characteristics. Additionally, oversampling is applied to training data to avoid overlooking the network outage in the prediction. From the evaluation using real data, we showed that the proposed method is effective for the prediction of network outages in a short period. Furthermore, we found that information regarding the instability of the optical signal is important for the prediction of network outages. The result herein can contribute to improving the availability of the network because the proposed method predicts network outages based on characteristics that are invisible from the IP layer.