A Transfer Learning Approach Based on Radar Rainfall for River Water-Level Prediction
Futo Ueda,
Hiroto Tanouchi,
Nobuyuki Egusa
et al.
Abstract:River water-level prediction is crucial for mitigating flood damage caused by torrential rainfall. In this paper, we attempt to predict river water levels using a deep learning model based on radar rainfall data instead of data from upstream hydrological stations. A prediction model incorporating a two-dimensional convolutional neural network (2D-CNN) and long short-term memory (LSTM) is constructed to exploit geographical and temporal features of radar rainfall data, and a transfer learning method using a new… Show more
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