Regional Streamflow Estimation by Standard Regional Dependence Function Approach

Altunkaynak, Abdüsselam; Özger, Mehmet; Şen, Zekâi̇

doi:10.1061/(asce)0733-9429(2005)131:11(1001)

Cited by 10 publications

(2 citation statements)

References 17 publications

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“…This process is repeated until establishing a successive layer [33]. Therefore, these kinds of networks are called feed forward back propagation (FF-BP) networks, which are the most popular supervised algorithm for training networks in prediction, pattern recognition, and nonlinear function fitting [13,41,40,25,2]. When using a FF-BP network, the sigmoid activation function is often preferred [34,24,17].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Flow forecast by SWAT model and ANN in Pracana basin, Portugal

Demirel¹,

Venâncio²,

Kahya³

2009

Advances in Engineering Software

157

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Section: Artificial Neural Networkmentioning

confidence: 99%

Flow forecast by SWAT model and ANN in Pracana basin, Portugal

Demirel¹,

Venâncio²,

Kahya³

2009

Advances in Engineering Software

157

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“…ALTUNKAYNAK et al (2003) presented a triple diagram method (TDM) based on the kriging technique for predicting future lake levels from two antecedent measurements, which are considered as independent variables. Later S¸EN et al (2004) and ALTUNKAYNAK et al (2005) applied kriging methodology to streamflow prediction. All the optimum interpolation techniques are applied geographically to one variable with longitude and latitude values.…”

Section: Optimum Interpolation Formulationmentioning

confidence: 99%

Space-time Interpolation by Combining Air Pollution and Meteorologic Variables

Şen

Altunkaynak

Özger

2006

Pure appl. geophys.

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In this paper, a technique is proposed in order to study triple time series. It combines the variable of interest, sulfur dioxide (SO 2 ) with two related meteorological variables. Hence, three variables measured at the same time points are jointly analyzed. Instead of using classical multiple time series analysis, it is suggested to consider the measurements of the two meteorological variables as coordinates of a two-dimensional space and the simultaneous observation of the third variable (associated SO 2 concentrations) at each pair of coordinates. Subsequently, well-known optimum interpolation is used for predicting the SO 2 concentrations on the basis of six meteorological variables. All the variables of the study are measured at the same times (all days in 2000) around the city of Istanbul, Turkey. The triple diagrams, in the form of contour maps, help to answer various questions concerning the SO 2 concentration variability with respect to meteorological variables. The same diagrams also provide a basis for the prediction of SO 2 concentrations. It is shown that the relative prediction error is less than 10%, which is acceptable for the practical studies.

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2006

Hydrological Processes

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Regional Streamflow Estimation by Standard Regional Dependence Function Approach

Cited by 10 publications

References 17 publications

Flow forecast by SWAT model and ANN in Pracana basin, Portugal

Flow forecast by SWAT model and ANN in Pracana basin, Portugal

Space-time Interpolation by Combining Air Pollution and Meteorologic Variables

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