Rainfall-runoff modeling using LSTM-based multi-state-vector sequence-to-sequence model

doi:10.1016/j.jhydrol.2021.126378

2021-07-01

PRE

DOI：10.1016/j.jhydrol.2021.126378

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摘要

En 中文

Rainfall-runoff modeling is a challenging and important nonlinear time series problem in hydrological sciences. Recently, among the data-driven rainfall-runoff models, those ones based on the long short-term memory (LSTM) network show good performance. Furthermore, LSTM-based sequence-to-sequence (LSTM-S2S) models achieve promising performance for multi-step-ahead runoff predictions. In this paper, for multi-day-ahead runoff predictions, we propose a novel data-driven model named LSTM-based multi-state-vector sequence-to-sequence (LSTM-MSV-S2S) rainfall-runoff model, which contains m multiple state vectors for m-step-ahead runoff predictions. It differs from the existing LSTM-S2S rainfall-runoff models using only one state vector and is more appropriate for multi-day-ahead runoff predictions. To show its performance and advantages, we compare it with two LSTM-S2S models by testing them on 673 basins of the Catchment Attributes and Meteorology for LargeSample Studies (CAMELS) data set. The results show that our LSTM-MSV-S2S model has better performance in general and thus using multiple state vectors is more appropriate for multi-day-ahead runoff predictions.

Keyword:

Rainfall-runoff model

Long short-term memory

Sequence-to-sequence

Recurrent neural network

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期刊

Journal of Hydrology

IF:

6.3

论文数:

2.4W

被引数:

9.8W

机构

Northwestern Polytechnical University

学者数:

4.6W

论文数: 3.7W

被引数: 5.3W

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