Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes

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.

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

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Συμπληρωματικό υλικό https://doi.org/10.6084/m9.figshare.7092824.v1

Εργασίες μας στις οποίες αναφέρεται αυτή η εργασία:

1. D. Koutsoyiannis, H. Yao, and A. Georgakakos, Medium-range flow prediction for the Nile: a comparison of stochastic and deterministic methods, Hydrological Sciences Journal, 53 (1), 142–164, doi:10.1623/hysj.53.1.142, 2008.
2. D. Koutsoyiannis, A random walk on water, Hydrology and Earth System Sciences, 14, 585–601, doi:10.5194/hess-14-585-2010, 2010.
3. H. Tyralis, and D. Koutsoyiannis, Simultaneous estimation of the parameters of the Hurst-Kolmogorov stochastic process, Stochastic Environmental Research & Risk Assessment, 25 (1), 21–33, 2011.
4. 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.
5. H. Tyralis, and D. Koutsoyiannis, A Bayesian statistical model for deriving the predictive distribution of hydroclimatic variables, Climate Dynamics, 42 (11-12), 2867–2883, doi:10.1007/s00382-013-1804-y, 2014.
6. 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.
7. Γ. Παπαχαραλάμπους, Θεωρητική και εμπειρική σύγκριση στοχαστικών μεθόδων και μεθόδων μηχανικής μάθησης στην πρόβλεψη υδρολογικών διεργασιών, Μεταπτυχιακή εργασία, 372 pages, Τομέας Υδατικών Πόρων και Περιβάλλοντος – Εθνικό Μετσόβιο Πολυτεχνείο, Αθήνα, Οκτώβριος 2016.
8. G. Papacharalampous, H. Tyralis, and D. Koutsoyiannis, Comparison between stochastic and machine learning methods for hydrological multi-step ahead forecasting: All forecasts are wrong!, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, 19, EGU2017-3068-2, doi:10.13140/RG.2.2.17205.47848, European Geosciences Union, 2017.
9. G. Papacharalampous, H. Tyralis, and D. Koutsoyiannis, Multi-step ahead streamflow forecasting for the operation of hydropower reservoirs, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, 19, EGU2017-3069, doi:10.13140/RG.2.2.27271.80801, European Geosciences Union, 2017.
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. G. Papacharalampous, H. Tyralis, and D. Koutsoyiannis, Forecasting of geophysical processes using stochastic and machine learning algorithms, European Water, 59, 161–168, 2017.
12. H. Tyralis, and G. Papacharalampous, Variable selection in time series forecasting using random forests, Algorithms, 10, 114, doi:10.3390/a10040114, 2017.
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.

Κατηγορίες: Ντετερμινισμός και στοχαστικότητα, Πιο πρόσφατες εργασίες, Στοχαστική