气候变化领域集成服务门户(CCPortal): Rainfall-runoff modelling using Long Short-Term Memory (LSTM) networks

CCPortal

DOI	10.5194/hess-22-6005-2018
	Rainfall-runoff modelling using Long Short-Term Memory (LSTM) networks
	Kratzert F.; Klotz D.; Brenner C.; Schulz K.; Herrnegger M.
发表日期	2018
ISSN	1027-5606
起始页码	6005
结束页码	6022
卷号	22 期号:11
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.
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
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159851
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
推荐引用方式 GB/T 7714	Kratzert F.,Klotz D.,Brenner C.,et al. Rainfall-runoff modelling using Long Short-Term Memory (LSTM) networks[J],2018,22(11).
APA	Kratzert F.,Klotz D.,Brenner C.,Schulz K.,&Herrnegger M..(2018).Rainfall-runoff modelling using Long Short-Term Memory (LSTM) networks.Hydrology and Earth System Sciences,22(11).
MLA	Kratzert F.,et al."Rainfall-runoff modelling using Long Short-Term Memory (LSTM) networks".Hydrology and Earth System Sciences 22.11(2018).