Generalized regression neural network in modelling river sediment yield

Authors:
Hikmet Kerem Cigizoglu;Murat Alp
Affiliations:
Istanbul Technical University, Civil Engineering Faculty, Division of Hydraulics, Maslak, Istanbul, Turkey;State Hydraulic Works, Regional Directorate, Küçükçamlica, Istanbul, Turkey
Venue:
Advances in Engineering Software
Year:
2006

Citing 1
Cited 10

Neural network PC tools: a practical guide

Neural network PC tools: a practical guide

Treatment of skewed multi-dimensional training data to facilitate the task of engineering neural models

Expert Systems with Applications: An International Journal
Using adaptive neuro-fuzzy inference system for hydrological time series prediction

Applied Soft Computing
Artificial Intelligence techniques: An introduction to their use for modelling environmental systems

Mathematics and Computers in Simulation
Implementing soft computing techniques to solve economic dispatch problem in power systems

Expert Systems with Applications: An International Journal
Using artificial neural networks for modeling suspended sediment concentration

MMACTEE'08 Proceedings of the 10th WSEAS International Conference on Mathematical Methods and Computational Techniques in Electrical Engineering
Monitoring event-based suspended sediment concentration by artificial neural network models

WSEAS Transactions on Computers
Neural network approach for estimating reference evapotranspiration from limited climatic data in Burkina Faso

WSEAS Transactions on Computers
How to select an optimal neural model of chemical reactivity?

Neurocomputing
An automotive generator fault diagnosis system using discrete wavelet transform and artificial neural network

Expert Systems with Applications: An International Journal
Generalized Regression Neural Networks and Feed Forward Neural Networks for prediction of scour depth around bridge piers

Advances in Engineering Software

Quantified Score

Hi-index	0.00

Visualization

Abstract

The majority of the artificial neural network applications in water resources involve the employment of feed forward back propagation method (FFBP). In this study another ANN algorithm, generalized regression neural network, GRNN, was used in river suspended sediment estimation. Generalized regression neural network does not require an iterative training procedure as in back propagation method. The GRNN simulations do not face the frequently encountered local minima problem in FFBP applications and GRNN does not generate estimates physically not plausible. The neural networks are trained using daily river flow and suspended sediment data belonging to Juniata Catchment in USA. The suspended sediment estimations provided by two ANN algorithms are compared with conventional sediment rating curve and multi linear regression method results. The mean squared error and the determination coefficient are used as comparison criteria. Also the estimated and observed sediment sums are examined in addition to two previously mentioned performance criteria. The ANN estimations are found significantly superior to conventional method results.