Artificial neural networks for sheet sediment transport

Tayfur, Gökmen

doi:10.1080/02626660209492997

Cited by 154 publications

(89 citation statements)

References 23 publications

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“…Studies on ANN application in the area of hydrology include rainfall-runoff modelling (Minns & Hall, 1996;Dawson & Wilby, 1998; streamflow prediction (Cigizoğlu, 2003;Sahoo & Ray, 2006) and suspended sediment modelling (Tayfur, 2002;Kisi, 2005). However, the application of ANN to evapotranspiration modelling is limited in the literature.…”

Section: Introductionmentioning

confidence: 99%

Generalized regression neural networks for evapotranspiration modelling

Kişi

2006

Hydrological Sciences Journal

165

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The potential is investigated of the generalized regression neural networks (GRNN) technique in modelling of reference evapotranspiration (ET 0 ) obtained using the FAO Penman-Monteith (PM) equation. Various combinations of daily climatic data, namely solar radiation, air temperature, relative humidity and wind speed, are used as inputs to the ANN so as to evaluate the degree of effect of each of these variables on ET 0 . In the first part of the study, a comparison is made between the estimates provided by the GRNN and those obtained by the Penman, Hargreaves and Ritchie methods as implemented by the California Irrigation Management System (CIMIS). The empirical models were calibrated using the standard FAO PM ET 0 values. The GRNN estimates are also compared with those of the calibrated models. Mean square error, mean absolute error and determination coefficient statistics are used as comparison criteria for the evaluation of the model performances. The GRNN technique (GRNN 1) whose inputs are solar radiation, air temperature, relative humidity and wind speed, gave mean square errors of 0.058 and 0.032 mm 2 day , and determination coefficients of 0.985 and 0.986 for the Pomona and Santa Monica stations (Los Angeles, USA), respectively. Based on the comparisons, it was found that the GRNN 1 model could be employed successfully in modelling the ET 0 process. The second part of the study investigates the potential of the GRNN and the empirical methods in ET 0 estimation using the nearby station data. Among the models, the calibrated Hargreaves was found to perform better than the others.

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Section: Introductionmentioning

confidence: 99%

Generalized regression neural networks for evapotranspiration modelling

Kişi

2006

Hydrological Sciences Journal

165

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“…During training, each connection multiplies the neuron output by a weight before the output enters the connected neuron. The combination of the weighted inputs can be expressed as (Tayfur, 2002):…”

Section: Regression Modelsmentioning

confidence: 99%

“…The sigmoid function is applied as an activation function in the training of network to understand if the activation of a neuron is strong enough and produces a successive output that is sent to other neurons as an input. The sigmoid function is represented below (Tayfur, 2002):…”

Section: Regression Modelsmentioning

confidence: 99%

“…Recent studies reveal that ANN has become an effective methodological approach for modelling sediment transport Abrahart and White (2001) carried out a study on the comparison of ANN and MLR techniques using small data sets, and proposed ANN was able to exceed the limitations of MLR method. Tayfur (2002) modelled the sheet sediment transport using ANN and tested the performance against that of the most commonly used physically-based models, whose transport capacities were based on flow velocity, shear stress, stream power, and unit stream power. The results revealed that ANN performed as well as the physically-based models for simulating nonsteady-state sediment loads from different slopes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Nehi̇rlerde Sedi̇ment Mi̇ktarinin Beli̇rlenmesi̇nde Ampi̇ri̇k Modelleme Tekni̇kleri̇ni̇n Değerlendi̇ri̇lmesi̇

Güven¹,

Akdoğan²

2016

Uludağ University Journal of the Faculty of Engineering

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Abstract:The sediment transport processes of streams have been the subject of research for many years. Sediment amount carried by a river is strongly correlated with the river's flow rate and sediment concentration. This study aims to represent this correlation and to estimate the sediment amount using four different modelling techniques: MLR, PLS, SVM, and ANN. Records of river flow, sediment concentration and sediment amount obtained from the Göksu River, located in the Eastern Mediterranean region of Turkey, are used as input data in the models. The aim of is this study is to evaluate the effectiveness of ANN modelling in the estimation of sediment amount carried by river flow. Fifty percent of the data are used as training set to develop the models. The other half of the data is used for verification set. The performance of the four models is evaluated by determination coefficient of prediction set (r 2 pred ). The results indicate that ANN is the most effective method (r 2 pred = 0.94), followed by SVM (r 2 pred = 0.72). MLR and PLS methods are the least effective techniques (r 2 pred = 0.67) for estimating sediment amount in the Göksu River. Therefore, ANN approach is further studied to propose the best configuration for the prediction of river sediment amount. Keywords

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“…ANNs have been recently employed for the solution of many hydraulic, hydrologic, and water resources problems ranging from rainfall runoff ͑Tokar and Johnson 1999; Rajurkar et al 2002͒ to sediment transport ͑Jain 2001Tayfur 2002;Nagy et al 2002͒ to solute transport ͑Aziz and Wong 1992; Lu et al 1998͒. However, for seepage through an earthfill dam they do not seem to have been applied.…”

Section: Introductionmentioning

confidence: 99%

Case Study: Finite Element Method and Artificial Neural Network Models for Flow through Jeziorsko Earthfill Dam in Poland

et al. 2005

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A finite element method ͑FEM͒ and an artificial neural network ͑ANN͒ model were developed to simulate flow through Jeziorsko earthfill dam in Poland. The developed FEM is capable of simulating two-dimensional unsteady and nonuniform flow through a nonhomogenous and anisotropic saturated and unsaturated porous body of an earthfill dam. For Jeziorsko dam, the FEM model had 5,497 triangular elements and 3,010 nodes, with the FEM network being made denser in the dam body and in the neighborhood of the drainage ditches. The ANN model developed for Jeziorsko dam was a feedforward three layer network employing the sigmoid function as an activator and the back-propagation algorithm for the network learning. The water levels on the upstream and downstream sides of the dam were input variables and the water levels in the piezometers were the target outputs in the ANN model. The two models were calibrated and verified using the piezometer data collected on a section of the Jeziorsko dam. The water levels computed by the models satisfactorily compared with those measured by the piezometers. The model results also revealed that the ANN model performed as good as and in some cases better than the FEM model. This case study offers insight into the adequacy of ANN as well as its competitiveness against FEM for predicting seepage through an earthfill dam body.

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Artificial neural networks for sheet sediment transport

Cited by 154 publications

References 23 publications

Generalized regression neural networks for evapotranspiration modelling

Generalized regression neural networks for evapotranspiration modelling

Nehi̇rlerde Sedi̇ment Mi̇ktarinin Beli̇rlenmesi̇nde Ampi̇ri̇k Modelleme Tekni̇kleri̇ni̇n Değerlendi̇ri̇lmesi̇

Case Study: Finite Element Method and Artificial Neural Network Models for Flow through Jeziorsko Earthfill Dam in Poland

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