Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS

Tyralis, Hristos; Papacharalampous, Georgia; Burnetas, Apostolos; Langousis, Andreas

doi:10.1016/j.jhydrol.2019.123957

Cited by 72 publications

(70 citation statements)

References 109 publications

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“…Under the stationarity and ergodicity assumptions (see e.g., Koutsoyiannis and Montanari 2015 for the implications of these assumptions in hydrological contexts), the trained error model can then be applied in the period T3 for converting a point hydrological prediction obtained using the same hydrological model with the same parameters into a probabilistic hydrological prediction. The error model could fall into the category of conditional distribution models (see e.g., Montanari and Brath 2004;Montanari and Grossi 2008) or the category of statistical learning regression models that can directly provide predictive quantiles instead of predictive PDFs (see e.g., Dogulu et al 2015;López López et al 2014;Papacharalampous et al 2019b;Tyralis et al 2019b), amongst other model categories.…”

Section: Methodological Background On Two-stage Post-processingmentioning

confidence: 99%

“…Here the interest is on probabilistic hydrological post-processing methodologies, in which the error model is estimated conditional upon the point prediction(s) of the hydrological model by using an independent segment (with respect to the one used for estimating the parameters of the hydrological model) extracted from the historical dataset. Various methodologies of this category are currently available (see e.g., Bock et al 2018;Bourgin et al 2015;Dogulu et al 2015;Farmer and Vogel 2016;López López et al 2014;Montanari and Brath 2004;Montanari and Grossi 2008;Montanari and Koutsoyiannis 2012;Solomatine and Shrestha 2009;Papacharalampous et al 2019b;Tyralis et al 2019b;Wani et al 2017), amongst other probabilistic hydrological modelling and hydrological forecasting methodologies based on the idea of integrating process-based models and statistical approaches (see e.g., Beven and Binley 1992; Hernández- López and Francés 2017;Kavetski et al 2002;2006, Krzysztofowicz 1999, 2001, 2002Kelly 2000, Krzysztofowicz andHerr 2001;Todini 2008; see also the review of Montanari 2011). Hereafter, we use the comprehensive term "two-stage" by Evin et al (2014) to imply that the parameters of a probabilistic hydrological post-processing methodology are estimated within two subsequent stages.…”

Section: Introductionmentioning

confidence: 99%

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Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: Methodology development and investigation using toy models

Papacharalampous

Koutsoyiannis

Montanari

2020

Advances in Water Resources

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We introduce an ensemble learning post-processing methodology for probabilistic hydrological modelling. This methodology generates numerous point predictions by applying a single hydrological model, yet with different parameter values drawn from the respective simulated posterior distribution. We call these predictions "sister predictions". Each sister prediction extending in the period of interest is converted into a probabilistic prediction using information about the hydrological model's errors. This information is obtained from a preceding period for which observations are available, and is exploited using a flexible quantile regression model.All probabilistic predictions are finally combined via simple quantile averaging to produce the output probabilistic prediction. The idea is inspired by the ensemble learning methods originating from the machine learning literature. The proposed methodology offers larger robustness in performance than basic post-processing methodologies using a single hydrological point prediction. It is also empirically proven to "harness the wisdom of the crowd" in terms of average interval score, i.e., the obtained quantile predictions score no worse -usually better− than the average score of the combined individual predictions. This proof is provided within toy examples, which can be used for gaining insight on how the methodology works and under which conditions it can optimally convert point hydrological predictions to probabilistic ones.A large-scale hydrological application is made in a companion paper.

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Section: Methodological Background On Two-stage Post-processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: Methodology development and investigation using toy models

Papacharalampous

Koutsoyiannis

Montanari

2020

Advances in Water Resources

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“…The quantile regression model is a balanced choice between interpretable and more flexible algorithms from the statistical learning literature. It has already been applied for post-processing hydrological predictions within hydrological modelling case studies (see e.g., Dogulu et al 2015, López López et al 2014, Solomatine and Shrestha 2009, Wani et al 2017, while its use is more common in the field of hydrological forecasting (see e.g., Tyralis et al 2019a and the references therein); see also the references in Dogulu et al (2015) for applications of this model in other geoscience concepts.…”

Section: Introductionmentioning

confidence: 99%

“…For benchmarking purposes, we also apply the working methodology using the linear regression model (see e.g., James et al 2013;Hastie et al 2009) as error model, and the two naïve probabilistic data-driven schemes. For the merits of using benchmarks in hydrological modelling, the reader is referred to Pappenberger et al (2015); see also benchmarking examples in Montanari and Brath (2004), Papacharalampous and Tyralis (2018), Papacharalampous et al (2018aPapacharalampous et al ( ,b,c, 2019a, Quilty et al (2019), Evin et al (2014), Sikorska et al (2015), Papacharalampous (2017, 2018), Tyralis et al ( , 2019a, and Xu et al (2018).…”

Section: Introductionmentioning

confidence: 99%

“…In the latter, we probabilistically solve monthly rainfall-runoff modelling problems for 270 catchments in the United States (US). Large-sample hydrological studies are increasingly carried out in the literature (see e.g., Bock et al 2018;Bourgin et al 2015;Coxon et al 2015;Farmer and Vogel 2016;Langousis et al 2016;Mouelhi et al 2006a,b;Papacharalampous et al 2018aPapacharalampous et al ,b, 2019aPapalexiou and Koutsoyiannis 2013;Perrin et al 2001;Ren et al 2016;Sawicz et al 2011;Tyralis and Koutsoyiannis 2017;Papacharalampous 2017, 2018;Tyralis et al 2017bTyralis et al , 2019aWeijs et al 2013;Xu et al 2018Xu et al , 2019, while this is the first study performing a largescale assessment of the MK blueprint methodology.…”

Section: Introductionmentioning

confidence: 99%

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Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale

Papacharalampous

Tyralis

Koutsoyiannis

et al. 2020

Advances in Water Resources

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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 postprocessing 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.

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Machine learning for hydrologic sciences: An introductory overview

Liang

2021

WIREs Water

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The hydrologic community has experienced a surge in interest in machine learning in recent years. This interest is primarily driven by rapidly growing hydrologic data repositories, as well as success of machine learning in various academic and commercial applications, now possible due to increasing accessibility to enabling hardware and software. This overview is intended for readers new to the field of machine learning. It provides a non-technical introduction, placed within a historical context, to commonly used machine learning algorithms and deep learning architectures. Applications in hydrologic sciences are summarized next, with a focus on recent studies. They include the detection of patterns and events such as land use change, approximation of hydrologic variables and processes such as rainfall-runoff modeling, and mining relationships among variables for identifying controlling factors. The use of machine learning is also discussed in the context of integrated with process-based modeling for parameterization, surrogate modeling, and bias correction. Finally, the article highlights challenges of extrapolating robustness, physical interpretability, and small sample size in hydrologic applications.

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Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS

Cited by 72 publications

References 109 publications

Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: Methodology development and investigation using toy models

Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: Methodology development and investigation using toy models

Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale

Machine learning for hydrologic sciences: An introductory overview

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