气候变化领域集成服务门户(CCPortal): Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale

CCPortal

DOI	10.1016/j.advwatres.2019.103470
	Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale
	Papacharalampous G.; Tyralis H.; Koutsoyiannis D.; Montanari A.
发表日期	2020
ISSN	0309-1708
卷号	136
英文摘要	Predictive hydrological uncertainty can be quantified by using ensemble methods. If properly formulated, these methods can offer improved predictive performance by combining multiple predictions. In this work, we use 50-year-long monthly time series observed in 270 catchments in the United States to explore the performances provided by an ensemble learning post-processing methodology for issuing probabilistic hydrological predictions. This methodology allows the utilization of flexible quantile regression models for exploiting information about the hydrological model's error. Its key differences with respect to basic two-stage hydrological post-processing methodologies using the same type of regression models are that (a) instead of a single point hydrological prediction it generates a large number of “sister predictions” (yet using a single hydrological model), and that (b) it relies on the concept of combining probabilistic predictions via simple quantile averaging. A major hydrological modelling challenge is obtaining probabilistic predictions that are simultaneously reliable and associated to prediction bands that are as narrow as possible; therefore, we assess both these desired properties of the predictions by computing their coverage probabilities, average widths and average interval scores. The results confirm the usefulness of the proposed methodology and its larger robustness with respect to basic two-stage post-processing methodologies. Finally, this methodology is empirically proven to harness the “wisdom of the crowd” in terms of average interval score, i.e., the average of the individual predictions combined by this methodology scores no worse –usually better− than the average of the scores of the individual predictions. © 2019 Elsevier Ltd
关键词	Catchments Climate models Forecasting Regression analysis Uncertainty analysis Ensemble learning Hydrological modeling Probabilistic prediction Quantile averaging Quantile regression Uncertainty quantifications Learning systems catchment experimental study hydrological modeling prediction probability quantitative analysis regression analysis time series analysis timescale uncertainty analysis United States
语种	英语
来源机构	Advances in Water Resources
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/131865
推荐引用方式 GB/T 7714	Papacharalampous G.,Tyralis H.,Koutsoyiannis D.,et al. Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale[J]. Advances in Water Resources,2020,136.
APA	Papacharalampous G.,Tyralis H.,Koutsoyiannis D.,&Montanari A..(2020).Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale.,136.
MLA	Papacharalampous G.,et al."Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale".136(2020).