Abstract
East Asian regions in the North Pacific have recently experienced severe riverine flood disasters. State-of-the-art neural networks are currently utilized as a quick-response flood model. Neural networks typically require ample time in the training process because of the use of numerous datasets. To reduce the computational costs, we introduced a transfer-learning approach to a neural-network-based flood model. For a concept of transfer leaning, once the model is pretrained in a source domain with large datasets, it can be reused in other target domains. After retraining parts of the model with the target domain datasets, the training time can be reduced due to reuse. A convolutional neural network (CNN) was employed because the CNN with transfer learning has numerous successful applications in two-dimensional image classification. However, our flood model predicts time-series variables (e.g., water level). The CNN with transfer learning requires a conversion tool from time-series datasets to image datasets in preprocessing. First, the CNN time-series classification was verified in the source domain with less than 10% errors for the variation in water level. Second, the CNN with transfer learning in the target domain efficiently reduced the training time by 1/5 of and a mean error difference by 15% of those obtained by the CNN without transfer learning, respectively. Our method can provide another novel flood model in addition to physical-based models.
Highlights
The East Asian regions along the North Pacific have recently experienced an increase in catastrophic flood disasters due to larger and stronger typhoons
We verified a typical classification by using the convolutional neural network (CNN) as a preliminary examination, which is often used for image analyses based on “true or false” binary classification
We created a new model that consists of the CNN with a transfer-learning approach and a conversion tool between the image dataset and time-series dataset
Summary
The East Asian regions along the North Pacific have recently experienced an increase in catastrophic flood disasters due to larger and stronger typhoons. To reduce and mitigate flood disasters, artificial neural network (ANN) models may be a beneficial tool for accurately and quickly forecasting riverine flood events in localized areas [1], in addition to conventional physical models [2]. In Japan, areas vulnerable to strong typhoons and heavy rainfall events have experienced severe riverine flood disasters in the last 3–4 years [3]. An overflow-risk warning system that is based on real-time observed data has been successfully working for most major rivers in Japan. A flood warning system that can forecast with a quick response has not been practically implemented in specific locations of rivers. If the specific time and location of inundation were forecasted by the flood warning system before the inundation occurred, most people may have been able to evacuate these locations in past flood disasters. When a forecast flood warning system is developed for practical use, an ANN model with deep learning is a candidate due
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