Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019WR025326 |
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
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ISSN | 0043-1397 |
EISSN | 1944-7973 |
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). |
条目包含的文件 | 条目无相关文件。 |
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