Author(s): Takehiko Ito; Ryo Kaneko; Tomoya Kataoka; Shiho Onomura; Yasuo Nihei
Linked Author(s):
Keywords: Flood prediction; Water-level; Deep learning; Recurrent neural network; LSTM
Abstract: In recent years, climate change has been responsible for many flood disasters. It is essential that protective measures should be developed against these. One of these measures is a flood forecasting system in which rising water-levels in rivers are predicted ahead of time. Although several researchers have applied artificial intelligence, and especially deep learning technology, to flood prediction, there is a lack of clarity regarding which deep learning approach is most effective in flood prediction. This study aimed to investigate the prediction of floods from water-level data by using a deep learning approach with data collected from the Kinu River. We adopted the LSTM (long short-term memory) algorithm, which is a type of recurrent neural network that readily reflects time-series data. In this study, we collected water-level data from five stations on the Kinu River, a branch of the Tone River, Japan. The results indicate that it is possible to utilize water-levels to predict flood events with a high level of accuracy.
Year: 2020