torabipodeh, H., godarzi, A., dehghani, R. (2019). Application of wavelet neural network in estimating suspended sediments of rivers, case study: Kashkan-Lorestan River. , 11(3), 650-660. doi: 10.22092/ijwmse.2018.116846.1411
hassan torabipodeh; ahmad godarzi; reza dehghani. "Application of wavelet neural network in estimating suspended sediments of rivers, case study: Kashkan-Lorestan River". , 11, 3, 2019, 650-660. doi: 10.22092/ijwmse.2018.116846.1411
torabipodeh, H., godarzi, A., dehghani, R. (2019). 'Application of wavelet neural network in estimating suspended sediments of rivers, case study: Kashkan-Lorestan River', , 11(3), pp. 650-660. doi: 10.22092/ijwmse.2018.116846.1411
torabipodeh, H., godarzi, A., dehghani, R. Application of wavelet neural network in estimating suspended sediments of rivers, case study: Kashkan-Lorestan River. , 2019; 11(3): 650-660. doi: 10.22092/ijwmse.2018.116846.1411

Application of wavelet neural network in estimating suspended sediments of rivers, case study: Kashkan-Lorestan River

مهندسی و مدیریت آبخیز
Article 7, Volume 11, Issue 3, October 2019, Pages 650-660    PDF (1.18 M)
Document Type: Research Paper
DOI: 10.22092/ijwmse.2018.116846.1411
Authors
hassan torabipodeh* 1; ahmad godarzi2; reza dehghani2
1Associate Professor of Water Engineering
2phd student
Abstract
Simulation and evaluation of river sediment is one of the important issues in water resources management. Measuring the amount of sediment in conventional methods generally involves a lot of time and cost and sometimes does not have sufficient accuracy. In this study, a wavelet neural network was used to estimate the sediments of the Kashkan River in Lorestan Province, and its results were compared with conventional smart methods such as artificial neural network. Parameters of discharge, temperature, water soluble solids content and precipitation as input and sediment discharge were selected as output during the monthly statistical period (1984-2013). Correlation coefficient, root mean squared error, and Nash Sutcliff coefficient were used to evaluate and compare the performance of the models. Results showed that the combined structure has been able to provide acceptable results in estimating sediment yield using two intelligent methods. However, in terms of accuracy, the wavelet neural network model with the highest correlation coefficient (0.850), the lowest root mean square error (0.151 tonday-1), and the Nash-Sutcliff criterion (0.758) were prioritized in the validation stage. Results also showed that the wavelet neural network model has a high ability to estimate the minimum and maximum values.
Keywords
Artificial neural network; Kashkan; Sediment; Water resources management; Wavelet neural network
References
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