globalchange  > 气候变化与战略
DOI: 10.5194/hess-22-6005-2018
论文题名:
Rainfall-runoff modelling using Long Short-Term Memory (LSTM) networks
作者: Kratzert F.; Klotz D.; Brenner C.; Schulz K.; Herrnegger M.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2018
卷: 22, 期:11
起始页码: 6005
结束页码: 6022
语种: 英语
Scopus关键词: Brain ; Catchments ; Digital storage ; Rain ; Runoff ; Snow ; Soil moisture ; Statistical tests ; Data-driven approach ; Data-driven model ; Hydrological modelling ; Input and outputs ; Long-term dependencies ; Model performance ; Rainfall - Runoff modelling ; Regional hydrological model ; Long short-term memory ; artificial neural network ; catchment ; data set ; experiment ; hydrological modeling ; model test ; rainfall-runoff modeling ; snow
英文摘要: Rainfall-runoff modelling is one of the key challenges in the field of hydrology. Various approaches exist, ranging from physically based over conceptual to fully data-driven models. In this paper, we propose a novel data-driven approach, using the Long Short-Term Memory (LSTM) network, a special type of recurrent neural network. The advantage of the LSTM is its ability to learn long-term dependencies between the provided input and output of the network, which are essential for modelling storage effects in e.g. catchments with snow influence. We use 241 catchments of the freely available CAMELS data set to test our approach and also compare the results to the well-known Sacramento Soil Moisture Accounting Model (SAC-SMA) coupled with the Snow-17 snow routine. We also show the potential of the LSTM as a regional hydrological model in which one model predicts the discharge for a variety of catchments. In our last experiment, we show the possibility to transfer process understanding, learned at regional scale, to individual catchments and thereby increasing model performance when compared to a LSTM trained only on the data of single catchments. Using this approach, we were able to achieve better model performance as the SAC-SMA+Snow-17, which underlines the potential of the LSTM for hydrological modelling applications. © Author(s) 2018.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/163134
Appears in Collections:气候变化与战略

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作者单位: Kratzert, F., Institute of Water Management, Hydrology and Hydraulic Engineering, University of Natural Resources and Life Sciences, Vienna, 1190, Austria; Klotz, D., Institute of Water Management, Hydrology and Hydraulic Engineering, University of Natural Resources and Life Sciences, Vienna, 1190, Austria; Brenner, C., Institute of Water Management, Hydrology and Hydraulic Engineering, University of Natural Resources and Life Sciences, Vienna, 1190, Austria; Schulz, K., Institute of Water Management, Hydrology and Hydraulic Engineering, University of Natural Resources and Life Sciences, Vienna, 1190, Austria; Herrnegger, M., Institute of Water Management, Hydrology and Hydraulic Engineering, University of Natural Resources and Life Sciences, Vienna, 1190, Austria

Recommended Citation:
Kratzert F.,Klotz D.,Brenner C.,et al. Rainfall-runoff modelling using Long Short-Term Memory (LSTM) networks[J]. Hydrology and Earth System Sciences,2018-01-01,22(11)
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