63 citations · 76 across the 4 of their papers we have counts for
4 papers
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…
Use of 1D-CNN for input data size reduction of LSTM in Hourly Rainfall-Runoff modeling
Kei Ishida, Ali Ercan, Takeyoshi Nagasato +2
An architecture consisting of a serial coupling of the one-dimensional convolutional neural network (1D-CNN) and the long short-term memory (LSTM) network, which is referred as CNN…
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…
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…