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

G. Papacharalampous, H. Tyralis, D. Koutsoyiannis, and A. Montanari, Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale, Advances in Water Resources, 136, 103470, doi:10.1016/j.advwatres.2019.103470, 2020.

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Το πλήρες κείμενο διατίθεται μόνο στο δίκτυο του ΕΜΠ λόγω νομικών περιορισμών

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Εργασίες μας στις οποίες αναφέρεται αυτή η εργασία:

1. D. Koutsoyiannis, and A. Montanari, Statistical analysis of hydroclimatic time series: Uncertainty and insights, Water Resources Research, 43 (5), W05429, doi:10.1029/2006WR005592, 2007.
2. D. Koutsoyiannis, C. Makropoulos, A. Langousis, S. Baki, A. Efstratiadis, A. Christofides, G. Karavokiros, and N. Mamassis, Climate, hydrology, energy, water: recognizing uncertainty and seeking sustainability, Hydrology and Earth System Sciences, 13, 247–257, doi:10.5194/hess-13-247-2009, 2009.
3. D. Koutsoyiannis, A random walk on water, Hydrology and Earth System Sciences, 14, 585–601, doi:10.5194/hess-14-585-2010, 2010.
4. A. Efstratiadis, and D. Koutsoyiannis, One decade of multiobjective calibration approaches in hydrological modelling: a review, Hydrological Sciences Journal, 55 (1), 58–78, doi:10.1080/02626660903526292, 2010.
5. D. Koutsoyiannis, Hurst-Kolmogorov dynamics and uncertainty, Journal of the American Water Resources Association, 47 (3), 481–495, doi:10.1111/j.1752-1688.2011.00543.x, 2011.
6. A. Montanari, and D. Koutsoyiannis, A blueprint for process-based modeling of uncertain hydrological systems, Water Resources Research, 48, W09555, doi:10.1029/2011WR011412, 2012.
7. S.M. Papalexiou, and D. Koutsoyiannis, Battle of extreme value distributions: A global survey on extreme daily rainfall, Water Resources Research, 49 (1), 187–201, doi:10.1029/2012WR012557, 2013.
8. A. Montanari, and D. Koutsoyiannis, Modeling and mitigating natural hazards: Stationarity is immortal!, Water Resources Research, 50 (12), 9748–9756, doi:10.1002/2014WR016092, 2014.
9. D. Koutsoyiannis, and A. Montanari, Negligent killing of scientific concepts: the stationarity case, Hydrological Sciences Journal, 60 (7-8), 1174–1183, doi:10.1080/02626667.2014.959959, 2015.
10. H. Tyralis, and D. Koutsoyiannis, On the prediction of persistent processes using the output of deterministic models, Hydrological Sciences Journal, 62 (13), 2083–2102, doi:10.1080/02626667.2017.1361535, 2017.
11. H. Tyralis, and G. Papacharalampous, Variable selection in time series forecasting using random forests, Algorithms, 10, 114, doi:10.3390/a10040114, 2017.
12. H. Tyralis, P. Dimitriadis, D. Koutsoyiannis, P.E. O’Connell, K. Tzouka, and T. Iliopoulou, On the long-range dependence properties of annual precipitation using a global network of instrumental measurements, Advances in Water Resources, 111, 301–318, doi:10.1016/j.advwatres.2017.11.010, 2018.
13. G. Papacharalampous, H. Tyralis, and D. Koutsoyiannis, One-step ahead forecasting of geophysical processes within a purely statistical framework, Geoscience Letters, 5, 12, doi:10.1186/s40562-018-0111-1, 2018.
14. G. Papacharalampous, H. Tyralis, and D. Koutsoyiannis, Predictability of monthly temperature and precipitation using automatic time series forecasting methods, Acta Geophysica, 66 (4), 807–831, doi:10.1007/s11600-018-0120-7, 2018.
15. G. Papacharalampous, H. Tyralis, and D. Koutsoyiannis, Univariate time series forecasting of temperature and precipitation with a focus on machine learning algorithms: a multiple-case study from Greece, Water Resources Management, 32 (15), 5207–5239, doi:10.1007/s11269-018-2155-6, 2018.
16. G. Papacharalampous, H. Tyralis, and D. Koutsoyiannis, Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes, Stochastic Environmental Research & Risk Assessment, doi:10.1007/s00477-018-1638-6, 2019.
17. G. Papacharalampous, H. Tyralis, A. Langousis, A. W. Jayawardena, B. Sivakumar, N. Mamassis, A. Montanari, and D. Koutsoyiannis, Probabilistic hydrological post-processing at scale: Why and how to apply machine-learning quantile regression algorithms, Water, doi:10.3390/w11102126, 2019.
18. G. Papacharalampous, D. Koutsoyiannis, and A. Montanari, Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: Methodology development and investigation using toy models, Advances in Water Resources, 136, 103471, doi:10.1016/j.advwatres.2019.103471, 2020.

Εργασίες μας που αναφέρονται σ' αυτή την εργασία:

1. D. Koutsoyiannis, Stochastics of Hydroclimatic Extremes - A Cool Look at Risk, 330 pages, Edition 0, National Technical University of Athens, Athens, 2020.
2. G. Papacharalampous, D. Koutsoyiannis, and A. Montanari, Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: Methodology development and investigation using toy models, Advances in Water Resources, 136, 103471, doi:10.1016/j.advwatres.2019.103471, 2020.