Artificial neural networks and high and low flows in various climate regimes

Panagoulia, D

dc.contributor.author	Panagoulia, D	en
dc.date.accessioned	2014-03-01T01:23:38Z
dc.date.available	2014-03-01T01:23:38Z
dc.date.issued	2006	en
dc.identifier.issn	0262-6667	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17060
dc.subject	Artificial neural network	en
dc.subject	Conceptual modelling	en
dc.subject	High flows	en
dc.subject	Linear least squares	en
dc.subject	Low flows	en
dc.subject	Simplex optimization	en
dc.subject.classification	Water Resources	en
dc.subject.other	Algorithms	en
dc.subject.other	Catchments	en
dc.subject.other	Computer simulation	en
dc.subject.other	Feedforward neural networks	en
dc.subject.other	Mathematical models	en
dc.subject.other	Optimization	en
dc.subject.other	Precipitation (meteorology)	en
dc.subject.other	Statistical methods	en
dc.subject.other	Conceptual modelling	en
dc.subject.other	High flows	en
dc.subject.other	Linear least squares	en
dc.subject.other	Low flows	en
dc.subject.other	Simplex optimization	en
dc.subject.other	Climatology	en
dc.subject.other	algorithm	en
dc.subject.other	artificial neural network	en
dc.subject.other	catchment	en
dc.subject.other	climate modeling	en
dc.subject.other	conceptual framework	en
dc.subject.other	flow modeling	en
dc.subject.other	least squares method	en
dc.subject.other	linearity	en
dc.subject.other	optimization	en
dc.subject.other	precipitation assessment	en
dc.subject.other	Eurasia	en
dc.subject.other	Europe	en
dc.subject.other	Greece	en
dc.subject.other	Southern Europe	en
dc.title	Artificial neural networks and high and low flows in various climate regimes	en
heal.type	journalArticle	en
heal.identifier.primary	10.1623/hysj.51.4.563	en
heal.identifier.secondary	http://dx.doi.org/10.1623/hysj.51.4.563	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	An algorithm coupling linear least squares and simplex optimization (LLSSIM) is used to examine the ability of a three-layer feedforward artificial neural network (ANN) to simulate the high and low flows in various climate regimes over a mountainous catchment (the Mesochora catchment in central Greece). The plot of the long-term annual catchment pseudo-precipitation (rain plus snowmelt) simulated by the snow accumulation and ablation model (SAA) of the US National Weather Service (US NWS) showed trends of three climatically distinct periods, described by clearly descending, rising and moderately descending segments in pseudo-precipitation. The ANN model was calibrated for each of the three climate types and each was validated against the others. A set of statistical measures and graphs adapted for high and low flows showed the robustness of the ANN model under various climates and transient conditions. The ANN model proved capable of simulating well the daily high and low flows when it is calibrated for increasing pseudo-precipitation and validated for moderately decreasing pseudo-precipitation. For the entire period, the ANN model provided a better simulation of high and low flows than the conceptual soil moisture accounting (SMA) model of the US NWS, which was also employed in this study. Because the ANN is not a physically-based model, it is by no means a substitute for the SMA model. However, it is concluded that the ANN approach is an effective alternative for daily high- and low-flow simulation and forecasting in climatically varied regimes, particularly in cases where the internal dynamics of the catchment do not require an explicit representation. Copyright © 2006 IAHS Press.	en
heal.publisher	IAHS PRESS, INST HYDROLOGY	en
heal.journalName	Hydrological Sciences Journal	en
dc.identifier.doi	10.1623/hysj.51.4.563	en
dc.identifier.isi	ISI:000239562700001	en
dc.identifier.volume	51	en
dc.identifier.issue	4	en
dc.identifier.spage	563	en
dc.identifier.epage	587	en