Multi-time-scale input approaches for hourly-scale rainfall–runoff modeling based on recurrent neural networks

This study proposes two effective approaches to reduce the required computational time of the training process for time-series modeling through a recurrent neural network (RNN) using multi-time-scale time-series data as input. One approach provides coarse and fine temporal resolutions of the input t...

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Bibliographic Details
Published in:Journal of hydroinformatics 2021-11, Vol.23 (6), p.1312-1324
Main Authors: Ishida, Kei, Kiyama, Masato, Ercan, Ali, Amagasaki, Motoki, Tu, Tongbi
Format: Article
Language:English
Subjects:
Computational efficiency
Computer applications
Computing time
deep learning
fine temporal resolution
Forecasting
Hydrology
Long short-term memory
long short-term memory network
Meteorological data
Modelling
Neural networks
Rain
Rainfall
Rainfall-runoff relationships
rainfall–runoff modeling
Recurrent neural networks
Resolution
Runoff
Temporal resolution
Time series
time-series modeling
Training
Variables
Watersheds
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Summary:This study proposes two effective approaches to reduce the required computational time of the training process for time-series modeling through a recurrent neural network (RNN) using multi-time-scale time-series data as input. One approach provides coarse and fine temporal resolutions of the input time-series data to RNN in parallel. The other concatenates the coarse and fine temporal resolutions of the input time-series data over time before considering them as the input to RNN. In both approaches, first, the finer temporal resolution data are utilized to learn the fine temporal scale behavior of the target data. Then, coarser temporal resolution data are expected to capture long-duration dependencies between the input and target variables. The proposed approaches were implemented for hourly rainfall–runoff modeling at a snow-dominated watershed by employing a long short-term memory network, which is a type of RNN. Subsequently, the daily and hourly meteorological data were utilized as the input, and hourly flow discharge was considered as the target data. The results confirm that both of the proposed approaches can reduce the required computational time for the training of RNN significantly. Lastly, one of the proposed approaches improves the estimation accuracy considerably in addition to computational efficiency.
ISSN:1464-7141
1465-1734
DOI:10.2166/hydro.2021.095