Multi-Time-Scale Input Approaches for Hourly-Scale Rainfall-Runoff Modeling based on Recurrent Neural Networks
This work addresses computational efficiency for hydrologists using RNNs, offering incremental improvements in training speed and accuracy.
The study tackled the high computational cost of training recurrent neural networks for time-series modeling by proposing two multi-time-scale input approaches, achieving up to 32.4 times faster training and improved estimation accuracy in hourly rainfall-runoff modeling.
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.