most citedExtension of Convolutional Neural Network along Temporal and Vertical Directions for Precipitation Downscaling

1 citations · 1 across the 1 of their papers we have counts for

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3 papers

cs.LG2021★ 1 cited

Extension of Convolutional Neural Network along Temporal and Vertical Directions for Precipitation Downscaling

Takeyoshi Nagasato, Kei Ishida, Ali Ercan +4

Deep learning has been utilized for the statistical downscaling of climate data. Specifically, a two-dimensional (2D) convolutional neural network (CNN) has been successfully appli…

physics.ao-ph2021

Capabilities of Deep Learning Models on Learning Physical Relationships: Case of Rainfall-Runoff Modeling with LSTM

Kazuki Yokoo, Kei Ishida, Ali Ercan +4

This study investigates the relationships which deep learning methods can identify between the input and output data. As a case study, rainfall-runoff modeling in a snow-dominated…

physics.ao-ph2021

Multi-Time-Scale Input Approaches for Hourly-Scale Rainfall-Runoff Modeling based on Recurrent Neural Networks

Kei Ishida, Masato Kiyama, Ali Ercan +2

This study proposes two straightforward yet effective approaches to reduce the required computational time of the training process for time-series modeling through a recurrent neur…