GSTDTAP  > 资源环境科学
DOI10.1029/2019WR025326
A Rainfall-Runoff Model With LSTM-Based Sequence-to-Sequence Learning
Xiang, Zhongrun1; Yan, Jun2; Demir, Ibrahim1
2020
发表期刊WATER RESOURCES RESEARCH
ISSN0043-1397
EISSN1944-7973
出版年2020
卷号56期号:1
文章类型Article
语种英语
国家USA; Peoples R China
英文摘要

Rainfall-runoff modeling is a complex nonlinear time series problem. While there is still room for improvement, researchers have been developing physical and machine learning models for decades to predict runoff using rainfall data sets. With the advancement of computational hardware resources and algorithms, deep learning methods such as the long short-term memory (LSTM) model and sequence-to-sequence (seq2seq) modeling have shown a good deal of promise in dealing with time series problems by considering long-term dependencies and multiple outputs. This study presents an application of a prediction model based on LSTM and the seq2seq structure to estimate hourly rainfall-runoff. Focusing on two Midwestern watersheds, namely, Clear Creek and Upper Wapsipinicon River in Iowa, these models were used to predict hourly runoff for a 24-hr period using rainfall observation, rainfall forecast, runoff observation, and empirical monthly evapotranspiration data from all stations in these two watersheds. The models were evaluated using the Nash-Sutcliffe efficiency coefficient, the correlation coefficient, statistical bias, and the normalized root-mean-square error. The results show that the LSTM-seq2seq model outperforms linear regression, Lasso regression, Ridge regression, support vector regression, Gaussian processes regression, and LSTM in all stations from these two watersheds. The LSTM-seq2seq model shows sufficient predictive power and could be used to improve forecast accuracy in short-term flood forecast applications. In addition, the seq2seq method was demonstrated to be an effective method for time series predictions in hydrology.


领域资源环境
收录类别SCI-E
WOS记录号WOS:000520132500035
WOS关键词ARTIFICIAL NEURAL-NETWORK ; INFORMATION-SYSTEM ; WATER-QUALITY
WOS类目Environmental Sciences ; Limnology ; Water Resources
WOS研究方向Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources
引用统计
文献类型期刊论文
条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/280449
专题资源环境科学
作者单位1.Univ Iowa, Dept Civil & Environm Engn, Iowa City, IA 52242 USA;
2.DHI China, Shanghai, Peoples R China
推荐引用方式
GB/T 7714
Xiang, Zhongrun,Yan, Jun,Demir, Ibrahim. A Rainfall-Runoff Model With LSTM-Based Sequence-to-Sequence Learning[J]. WATER RESOURCES RESEARCH,2020,56(1).
APA Xiang, Zhongrun,Yan, Jun,&Demir, Ibrahim.(2020).A Rainfall-Runoff Model With LSTM-Based Sequence-to-Sequence Learning.WATER RESOURCES RESEARCH,56(1).
MLA Xiang, Zhongrun,et al."A Rainfall-Runoff Model With LSTM-Based Sequence-to-Sequence Learning".WATER RESOURCES RESEARCH 56.1(2020).
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