Multi-Time-Scale Input Approaches for Hourly-Scale Rainfall-Runoff Modeling based on Recurrent Neural Networks

TL;DR

This paper introduces two multi-time-scale input methods for RNN-based hourly rainfall-runoff modeling that significantly reduce training time and improve accuracy, especially using LSTM networks in snow-dominated watersheds.

Contribution

The paper presents novel multi-time-scale input strategies for RNNs that enhance training efficiency and accuracy in rainfall-runoff modeling.

Findings

Training time reduced by up to 32.4 times.

One approach improves estimation accuracy.

Effective for hourly rainfall-runoff modeling.

Abstract

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.