It is important for traffic management to understand unusual conditions or road abnormalities caused by natural disasters (such as an earthquake or heavy rain) or traffic congestion caused by special events (such as festivals at tourist spots). Among these, we focused on flooded roads as unusual events and proposed a method to detect it automatically, using deep-learning methods from driving videos. Because such unusual events rarely occur, the amount of training data for deep learning is usually insufficient. Therefore, we propose a data-augmentation approach using Generative Adversarial Networks (GANs) to solve the problem. To effectively augment the data, we propose a multi-domain image-to-image transformation by GANs. In addition, to increase the robustness of the image transformation, we newly introduce semantic information. We synthesized a new dataset using GANs and verified the performance of our method by detecting flooded scenes.