  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 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 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,
finer temporal resolution data are utilized to learn the fine temporal scale
behavior of the target data. Next, 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 and short-term
memory (LSTM) network, which is a newer 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 computational time for the training of
RNN significantly (up to 32.4 times). Furthermore, one of the proposed
approaches improves the estimation accuracy.
