气候变化领域集成服务门户(CCPortal): Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model

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DOI	10.5194/hess-24-4641-2020
	Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model
	Teweldebrhan A.T.; Schuler T.V.; Burkhart J.F.; Hjorth-Jensen M.
发表日期	2020
ISSN	1027-5606
起始页码	4641
结束页码	4658
卷号	24 期号:9
英文摘要	Monte Carlo (MC) methods have been widely used in uncertainty analysis and parameter identification for hydrological models. The main challenge with these approaches is, however, the prohibitive number of model runs required to acquire an adequate sample size, which may take from days to months -especially when the simulations are run in distributed mode. In the past, emulators have been used to minimize the computational burden of the MC simulation through direct estimation of the residual-based response surfaces. Here, we apply emulators of an MC simulation in parameter identification for a distributed conceptual hydrological model using two likelihood measures, i.e. the absolute bias of model predictions (Score) and another based on the time-relaxed limits of acceptability concept (pLoA). Three machine-learning models (MLMs) were built using model parameter sets and response surfaces with a limited number of model realizations (4000). The developed MLMs were applied to predict pLoA and Score for a large set of model parameters (95 000). The behavioural parameter sets were identified using a time-relaxed limits of acceptability approach, based on the predicted pLoA values, and applied to estimate the quantile streamflow predictions weighted by their respective Score. The three MLMs were able to adequately mimic the response surfaces directly estimated from MC simulations with an R2 value of 0.7 to 0.92. Similarly, the models identified using the coupled machine-learning (ML) emulators and limits of acceptability approach have performed very well in reproducing the median streamflow prediction during the calibration and validation periods, with an average Nash-Sutcliffe efficiency value of 0.89 and 0.83, respectively. © Author(s) 2020.
语种	英语
scopus关键词	Climate models; Forecasting; Hydrology; Machine learning; Monte Carlo methods; Stream flow; Surface properties; Uncertainty analysis; Calibration and validations; Computational burden; Distributed hydrological model; Hydrological modeling; Hydrological models; Machine learning models; Model parameter sets; Streamflow prediction; Parameter estimation; hydrological modeling; identification method; machine learning; parameter estimation
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/159298
作者单位	Teweldebrhan, A.T., Department of Geosciences, University of Oslo, Oslo, Norway; Schuler, T.V., Department of Geosciences, University of Oslo, Oslo, Norway; Burkhart, J.F., Department of Geosciences, University of Oslo, Oslo, Norway; Hjorth-Jensen, M., Department of Geosciences, University of Oslo, Oslo, Norway, Department of Physics and Astronomy, Michigan State UniversityMI, United States
推荐引用方式 GB/T 7714	Teweldebrhan A.T.,Schuler T.V.,Burkhart J.F.,et al. Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model[J],2020,24(9).
APA	Teweldebrhan A.T.,Schuler T.V.,Burkhart J.F.,&Hjorth-Jensen M..(2020).Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model.Hydrology and Earth System Sciences,24(9).
MLA	Teweldebrhan A.T.,et al."Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model".Hydrology and Earth System Sciences 24.9(2020).