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

Abstract

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.

Application of Rainfall-Runoff Simulation Based on the NARX Dynamic Neural Network Model

“…The application of the concept of signal delay in neurobiological information processing has led to the rapid development of dynamic neural networks [30]. Taking the time delay into account through feedback connections, recurrent neural networks (RNN) are more powerful and plausible than other static neural networks, can be trained to learn time-varying patterns and are regarded as an effective approach to simulate complex dynamic hydrological processes [31,32]. The Elman neural network (ENN), long short-term memory (LSTM) and the nonlinear autoregressive model with exogenous input (NARX) are widely used RNN models in hydrological time series forecasts.…”

A Systematic Review of Deep Learning Applications in Streamflow Data Augmentation and Forecasting

“…They used a meteorological dataset that consists of precipitation, air temperature, evapotranspiration, and long-and short-wave radiation. Furthermore,Ishida et al (2021b) proposed two effective methods to decrease the computation time in LSTM models where streamflow forecasting is the aim.Similarly, Nakatani et al (2021) combined RNNs with CNNs to model streamflow using rainfall distribution data (XRAIN) for the Katsura River basin, Kyoto, Japan. Moreover, developed Integrated Flood Analysis System (IFAS) model to simulate runoff in the Tokachi River, Japan.…”