Medium-range flow prediction for the Nile: A comparison of stochastic and deterministic methods

Koutsoyiannis, D; Yao, H; Georgakakos, A

dc.contributor.author	Koutsoyiannis, D	en
dc.contributor.author	Yao, H	en
dc.contributor.author	Georgakakos, A	en
dc.date.accessioned	2014-03-01T01:28:45Z
dc.date.available	2014-03-01T01:28:45Z
dc.date.issued	2008	en
dc.identifier.issn	0262-6667	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18956
dc.subject	Artificial neural networks	en
dc.subject	Hurst phenomenon	en
dc.subject	Linearity and nonlinearity	en
dc.subject	Maximum entropy	en
dc.subject	Nile	en
dc.subject	Stochastic vs deterministic models	en
dc.subject.classification	Water Resources	en
dc.subject.other	Computer simulation	en
dc.subject.other	Mathematical models	en
dc.subject.other	Neural networks	en
dc.subject.other	Random processes	en
dc.subject.other	Statistical methods	en
dc.subject.other	Flow prediction	en
dc.subject.other	Hydrological stochastic modeling	en
dc.subject.other	Stochastic prediction	en
dc.subject.other	Hydrology	en
dc.subject.other	artificial neural network	en
dc.subject.other	flow modeling	en
dc.subject.other	hydrological modeling	en
dc.subject.other	linearity	en
dc.subject.other	maximum entropy analysis	en
dc.subject.other	nonlinearity	en
dc.subject.other	stochasticity	en
dc.subject.other	Africa	en
dc.subject.other	East Africa	en
dc.subject.other	Nile [Uganda]	en
dc.subject.other	Sub-Saharan Africa	en
dc.subject.other	Uganda	en
dc.title	Medium-range flow prediction for the Nile: A comparison of stochastic and deterministic methods	en
heal.type	journalArticle	en
heal.identifier.primary	10.1623/hysj.53.1.142	en
heal.identifier.secondary	http://dx.doi.org/10.1623/hysj.53.1.142	en
heal.language	English	en
heal.publicationDate	2008	en
heal.abstract	Due to its great importance, the availability of long flow records, contemporary as well as older, and the additional historical information of its behaviour, the Nile is an ideal test case for identifying and understanding hydrological behaviours, and for model development. Such behaviours include the long-term persistence, which historically has motivated the discovery of the Hurst phenomenon and has put into question classical statistical results and typical stochastic models. Based on the empirical evidence from the exploration of the Nile flows and on the theoretical insights provided by the principle of maximum entropy, a concept newly employed in hydrological stochastic modelling, an advanced yet simple stochastic methodology is developed. The approach is focused on the prediction of the Nile flow a month ahead, but the methodology is general and can be applied to any type of stochastic prediction. The stochastic methodology is also compared with deterministic approaches, specifically an analogue (local nonlinear chaotic) model and a connectionist (artificial neural network) model based on the same flow record. All models have good performance with the stochastic model outperforming in prediction skills and the analogue model in simplicity. In addition, the stochastic model has other elements of superiority such as the ability to provide long-term simulations and to improve understanding of natural behaviours. Copyright © 2008 IAHS Press.	en
heal.publisher	IAHS PRESS, INST HYDROLOGY	en
heal.journalName	Hydrological Sciences Journal	en
dc.identifier.doi	10.1623/hysj.53.1.142	en
dc.identifier.isi	ISI:000253632500010	en
dc.identifier.volume	53	en
dc.identifier.issue	1	en
dc.identifier.spage	142	en
dc.identifier.epage	164	en