Μεσοπρόθεσμη πρόγνωση της παροχής του Νείλου: Σύγκριση στοχαστικών και προσδιοριστικών μεθόδων

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.

[Μεσοπρόθεσμη πρόγνωση της παροχής του Νείλου: Σύγκριση στοχαστικών και προσδιοριστικών μεθόδων]

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[Αγγλικά]

Εξαιτίας της μεγάλης του σπουδαιότητας, και της διαθεσιμότητας μεγάλου μήκους παρατηρημένων χρονοσειρών αλλά και ιστορικών πληροφοριών για τη δίαιτά του, ο Νείλος είναι μια ιδεώδης περίπτωση για την ταυτοποίηση και κατανόηση υδρολογικών συμπεριφορών και για την ανάπτυξη μοντέλων. Σε αυτές τις συμπεριφορές περιλαμβάνεται η μακροπρόθεσμη εμμονή, η οποία ιστορικά ήταν το κίνητρο για την ανακάλυψη του φαινομένου Hurst και έθεσε υπό αμφισβήτηση τα κλασικά στατιστικά αποτελέσματα και τα τυπικά στοχαστικά μοντέλα. Με βάση εμπειρικά δεδομένα από την εξερεύνηση των παροχών του Νείλου και θεωρητικές αναλύσεις που υποστηρίζονται από την αρχή της μέγιστης εντροπίας, μια έννοια που πρόσφατα επιστρατεύτηκε στην ανάπτυξη υδρολογικών στοχαστικών μοντέλων, αναπτύσσεται μια προχωρημένη και ταυτόχρονα απλή στοχαστική μεθοδολογία. Αυτή εστιάζεται στην πρόβλεψη της παροχής του Νείλου μετά από ένα μήνα αλλά είναι αρκετά γενικευμένη. Η στοχαστική μεθοδολογία συγκρίνεται επίσης με προσδιοριστικές προσεγγίσεις και συγκεκριμένα με ένα (τοπικό μη γραμμικό χαοτικό) μοντέλο αναλόγου και με ένα συνδετικό μοντέλο (τεχνητό νευρωνικό δίκτυο), όπου και τα δύο εφαρμόζονται στο ίδιο δείγμα παροχών. Όλα τα μοντέλα έχουν καλή επίδοση, με το στοχαστικό μοντέλο να υπερέχει σε προγνωστική ικανότητα και το μοντέλο αναλόγου σε απλότητα. Επιπλέον, το στοχαστικό μοντέλο έχει άλλα στοιχεία υπεροχής, όπως τη δυνατότητα να παρέχει μακροπρόθεσμες προσομοιώσεις και να βελτιώνει την κατανόηση των φυσικών συμπεριφορών.

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Βλέπε επίσης: http://dx.doi.org/10.1623/hysj.53.1.142

Εργασίες μας στις οποίες αναφέρεται αυτή η εργασία:

1. D. Koutsoyiannis, Optimal decomposition of covariance matrices for multivariate stochastic models in hydrology, Water Resources Research, 35 (4), 1219–1229, doi:10.1029/1998WR900093, 1999.
2. D. Koutsoyiannis, A generalized mathematical framework for stochastic simulation and forecast of hydrologic time series, Water Resources Research, 36 (6), 1519–1533, doi:10.1029/2000WR900044, 2000.
3. D. Koutsoyiannis, Coupling stochastic models of different time scales, Water Resources Research, 37 (2), 379–391, doi:10.1029/2000WR900200, 2001.
4. D. Koutsoyiannis, The Hurst phenomenon and fractional Gaussian noise made easy, Hydrological Sciences Journal, 47 (4), 573–595, doi:10.1080/02626660209492961, 2002.
5. D. Koutsoyiannis, Climate change, the Hurst phenomenon, and hydrological statistics, Hydrological Sciences Journal, 48 (1), 3–24, doi:10.1623/hysj.48.1.3.43481, 2003.
6. D. Koutsoyiannis, Uncertainty, entropy, scaling and hydrological stochastics, 1, Marginal distributional properties of hydrological processes and state scaling, Hydrological Sciences Journal, 50 (3), 381–404, doi:10.1623/hysj.50.3.381.65031, 2005.
7. D. Koutsoyiannis, Uncertainty, entropy, scaling and hydrological stochastics, 2, Time dependence of hydrological processes and time scaling, Hydrological Sciences Journal, 50 (3), 405–426, doi:10.1623/hysj.50.3.405.65028, 2005.
8. A. Langousis, and D. Koutsoyiannis, A stochastic methodology for generation of seasonal time series reproducing overyear scaling behaviour, Journal of Hydrology, 322, 138–154, 2006.
9. D. Koutsoyiannis, On the quest for chaotic attractors in hydrological processes, Hydrological Sciences Journal, 51 (6), 1065–1091, doi:10.1623/hysj.51.6.1065, 2006.
10. D. Koutsoyiannis, and A. Georgakakos, Lessons from the long flow records of the Nile: determinism vs indeterminism and maximum entropy, 20 Years of Nonlinear Dynamics in Geosciences, Rhodes, Greece, doi:10.13140/RG.2.2.10996.14727, 2006.
11. 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.

Εργασίες μας που αναφέρονται σ' αυτή την εργασία:

1. D. Koutsoyiannis, A. Montanari, H. F. Lins, and T.A. Cohn, Climate, hydrology and freshwater: towards an interactive incorporation of hydrological experience into climate research—DISCUSSION of “The implications of projected climate change for freshwater resources and their management”, Hydrological Sciences Journal, 54 (2), 394–405, doi:10.1623/hysj.54.2.394, 2009.
2. D. Koutsoyiannis, Z. W. Kundzewicz, F. Watkins, and C. Gardner, Something old, something new, something red, something blue, Hydrological Sciences Journal, 55 (1), 1–3, 2010.
3. 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.
4. D. Koutsoyiannis, A. Paschalis, and N. Theodoratos, Two-dimensional Hurst-Kolmogorov process and its application to rainfall fields, Journal of Hydrology, 398 (1-2), 91–100, doi:10.1016/j.jhydrol.2010.12.012, 2011.
5. S.M. Papalexiou, D. Koutsoyiannis, and A. Montanari, Can a simple stochastic model generate rich patterns of rainfall events?, Journal of Hydrology, 411 (3-4), 279–289, 2011.
6. D. Koutsoyiannis, Hydrology and Change, Hydrological Sciences Journal, 58 (6), 1177–1197, doi:10.1080/02626667.2013.804626, 2013.
7. D. Koutsoyiannis, Entropy: from thermodynamics to hydrology, Entropy, 16 (3), 1287–1314, doi:10.3390/e16031287, 2014.
8. A. Efstratiadis, Y. Dialynas, S. Kozanis, and D. Koutsoyiannis, A multivariate stochastic model for the generation of synthetic time series at multiple time scales reproducing long-term persistence, Environmental Modelling and Software, 62, 139–152, doi:10.1016/j.envsoft.2014.08.017, 2014.
9. A. Sikorska, A. Montanari, and D. Koutsoyiannis, Estimating the uncertainty of hydrological predictions through data-driven resampling techniques, Journal of Hydrologic Engineering (ASCE), 20 (1), doi:10.1061/(ASCE)HE.1943-5584.0000926, 2015.
10. D. Koutsoyiannis, Generic and parsimonious stochastic modelling for hydrology and beyond, Hydrological Sciences Journal, 61 (2), 225–244, doi:10.1080/02626667.2015.1016950, 2016.
11. P. Dimitriadis, D. Koutsoyiannis, and K. Tzouka, Predictability in dice motion: how does it differ from hydrometeorological processes?, Hydrological Sciences Journal, 61 (9), 1611–1622, doi:10.1080/02626667.2015.1034128, 2016.
12. 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.
13. I. Tsoukalas, C. Makropoulos, and A. Efstratiadis, Stochastic simulation of periodic processes with arbitrary marginal distributions, 15th International Conference on Environmental Science and Technology (CEST2017), Rhodes, Global Network on Environmental Science and Technology, 2017.
14. G. Papacharalampous, H. Tyralis, and D. Koutsoyiannis, Forecasting of geophysical processes using stochastic and machine learning algorithms, European Water, 59, 161–168, 2017.
15. P. Dimitriadis, and D. Koutsoyiannis, Stochastic synthesis approximating any process dependence and distribution, Stochastic Environmental Research & Risk Assessment, 32 (6), 1493–1515, doi:10.1007/s00477-018-1540-2, 2018.
16. I. Tsoukalas, A. Efstratiadis, and C. Makropoulos, Stochastic periodic autoregressive to anything (SPARTA): Modelling and simulation of cyclostationary processes with arbitrary marginal distributions, Water Resources Research, 54 (1), 161–185, WRCR23047, doi:10.1002/2017WR021394, 2018.
17. 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.
18. G. Koudouris, P. Dimitriadis, T. Iliopoulou, N. Mamassis, and D. Koutsoyiannis, A stochastic model for the hourly solar radiation process for application in renewable resources management, Advances in Geosciences, 45, 139–145, doi:10.5194/adgeo-45-139-2018, 2018.
19. 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.
20. P. Dimitriadis, K. Tzouka, D. Koutsoyiannis, H. Tyralis, A. Kalamioti, E. Lerias, and P. Voudouris, Stochastic investigation of long-term persistence in two-dimensional images of rocks, Spatial Statistics, 29, 177–191, doi:10.1016/j.spasta.2018.11.002, 2019.
21. 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.
22. T. Iliopoulou, C. Aguilar , B. Arheimer, M. Bermúdez, N. Bezak, A. Ficchi, D. Koutsoyiannis, J. Parajka, M. J. Polo, G. Thirel, and A. Montanari, A large sample analysis of European rivers on seasonal river flow correlation and its physical drivers, Hydrology and Earth System Sciences, 23, 73–91, doi:10.5194/hess-23-73-2019, 2019.
23. 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.
24. G.-F. Sargentis, P. Dimitriadis, and D. Koutsoyiannis, Aesthetical issues of Leonardo Da Vinci’s and Pablo Picasso’s paintings with stochastic evaluation, Heritage, 3 (2), 283–305, doi:10.3390/heritage3020017, 2020.
25. D. Koutsoyiannis, and Z. W. Kundzewicz, Atmospheric temperature and CO₂: Hen-or-egg causality?, Sci, 2 (4), 83, doi:10.3390/sci2040083, 2020.
26. G.-F. Sargentis, P. Dimitriadis, T. Iliopoulou, and D. Koutsoyiannis, A stochastic view of varying styles in art paintings, Heritage, 4, 21, doi:10.3390/heritage4010021, 2021.
27. P. Dimitriadis, D. Koutsoyiannis, T. Iliopoulou, and P. Papanicolaou, A global-scale investigation of stochastic similarities in marginal distribution and dependence structure of key hydrological-cycle processes, Hydrology, 8 (2), 59, doi:10.3390/hydrology8020059, 2021.
28. D. Koutsoyiannis, C. Onof, A. Christofides, and Z. W. Kundzewicz, Revisiting causality using stochastics: 1.Theory, Proceedings of The Royal Society A, 478 (2261), 20210835, doi:10.1098/rspa.2021.0835, 2022.

Άλλες εργασίες που αναφέρονται σ' αυτή την εργασία: Δείτε τις στο Google Scholar ή στο ResearchGate

Άλλες εργασίες που αναφέρονται σ' αυτή την εργασία (αυτός ο κατάλογος μπορεί να μην είναι ενημερωμένος):

1. Benyahya, L., Α. St-Hilaire, T.B.M.J. Ouarda, Β. Bobee and J. Dumas, Comparison of non-parametric and parametric water temperature models on the Nivelle River, France, Hydrological Sciences Journal, 53(3), 640-655, 2008.
2. Aytek, A., A. Guven, M.I. Yuce and H. Aksoy, An explicit neural network formulation for evapotranspiration, Hydrological Sciences Journal, 53 (4), 893-904, 2008.
3. El-Shafie, A., A. Noureldin, M. Taha, and H. Basri, Neural network model for Nile River inflow forecasting based on correlation analysis of historical inflow data, Journal of Applied Sciences, 8(24), 4487-4499, 2008.
4. Ozger, M., Comparison of fuzzy inference systems for streamflow prediction, Hydrological Sciences Journal, 54(2), 261-273, 2009.
5. Hamed, K. H., Effect of persistence on the significance of Kendall’s tau as a measure of correlation between natural time series, The European Physical Journal, 174 (1), 65-79, 2009.
6. #Kileshye Onema, J.-M., Z. Katambara and A. Taigbenu, Shuffled complex evolution and multi-linear approaches to flow prediction in the equatorial Nile basin, First Annual Nile Basin Research Conference, Dar Es Salaam, Tanzania, 2009.
7. Hassan, S. A. and M. R. K. Ansari, Nonlinear analysis of seasonality and stochasticity of the Indus River, Hydrol. Sci. J., 55(2), 250–265, 2010.
8. Londhe, S., and S. Charhate, Comparison of data-driven modelling techniques for river flow forecasting, Hydrol. Sci. J., 55(7), 1163–1174, 2010.
9. Archfield, S. A., and R. M. Vogel, Map correlation method: Selection of a reference streamgage to estimate daily streamflow at ungaged catchments, Water Resour. Res., 46, W10513, doi: 10.1029/2009WR008481, 2010.
10. Di Baldassarre, G., M. Elshamy, A. van Griensven, E. Soliman, M. Kigobe, P. Ndomba, J. Mutemi, F. Mutua, S. Moges, J.-Q. Xuan, D. Solomatine and S. Uhlenbrook, Future hydrology and climate in the River Nile basin: a review, Hydrol. Sci. J., 56(2), 199-211, 2011.
11. Muluye, G. Y., Improving long-range hydrological forecasts with extended Kalman filters, Hydrol. Sci. J., 56 (7), 1118–1128, 2011.
12. Ndiritu, J., A variable length block bootstrap for multi-site synthetic streamflow generation, Hydrol. Sci. J., 56 (3), 362-379, 2011.
13. Swain, A., Challenges for water sharing in the Nile basin: changing geo-politics and changing climate, Hydrol. Sci. J., 56 (4), 687–702, 2011.
14. Di Baldassarre, G., M. Elshamy, A. van Griensven, E. Soliman, M. Kigobe, P. Ndomba, J. Mutemi, F. Mutua, S. Moges, Y. Xuan, D. Solomatine and S. Uhlenbrook, A Critical Discussion of Recent Studies Evaluating the Impacts of Climate Change on Water Resources in the Nile basin, Nile Basin Water Science & Engineering Journal, 4 (2), 94-100, 2011.
15. Kileshye Onema, J.-M., A., Taigbenu and J. Ndiritu, J.: Classification and flow prediction in a data-scarce watershed of the Equatorial Nile region, Hydrol. Earth Syst. Sci., 16, 1435-1443, 2012.
16. Costa, A.C., A. Bronstert and D. Kneis, Probabilistic flood forecasting for a mountainous headwater catchment using a nonparametric stochastic dynamic approach, Hydrological Sciences Journal, 57 (1), 10–25, 2012.
17. Boukharouba, K., Annual stream flow simulation by ARMA processes and prediction by Kalman filter, Arab J. Geosci., 6 (7), 2193-2201, 2013.
18. Hrachowitz, M., H.H.G. Savenije, G. Blöschl, J.J. McDonnell, M. Sivapalan, J.W. Pomeroy, B. Arheimer, T. Blume, M.P. Clark, U. Ehret, F. Fenicia, J.E. Freer, A. Gelfan, H.V. Gupta, D.A. Hughes, R.W. Hut, A. Montanari, S. Pande, D. Tetzlaff, P.A. Troch, S. Uhlenbrook, T. Wagener, H.C. Winsemius, R.A. Woods, E. Zehe, and C. Cudennec, A decade of Predictions in Ungauged Basins (PUB) — a review, Hydrological Sciences Journal, 58(6), 1198-1255, 2013.
19. Markovic, D., and M. Koch, Stream response to precipitation variability: A spectral view based on analysis and modelling of hydrological cycle components, Hydrological Processes, 29 (7), 1806-1816, 2015.
20. Svensson, C.,Seasonal river flow forecasts for the United Kingdom using persistence and historical analogues, Hydrological Sciences Journal, 10.1080/02626667.2014.992788, 2015.

Κατηγορίες: Βιβλιογραφία μαθήματος: Στοχαστικές μέθοδοι, Ντετερμινισμός και στοχαστικότητα, Εντροπία, Δυναμική Hurst-Kolmogorov, Ομοιοθεσία