Predicting Longitudinal Dispersion Coefficient in Natural Streams by Artificial Neural Network

By Gökmen Tayfur, Vijay P. Singh. 2005

118 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/3b8a27be80d92220bb1035d9b78beeb19a6bdeed

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

1. Wenxin Huai
2. Y. Zeng
3. Gökmen Tayfur
4. Vijay P. Singh

In Environmental Engineering

1. Wenxin Huai
2. Y. Zeng
3. Vijay P. Singh
4. Gökmen Tayfur
5. Shenglian Guo
6. Shih-Chieh Kao
7. Rao S. Govindaraju
8. Ozgur Kisi

In Water Science and Technology

1. Wenxin Huai
2. Y. Zeng
3. Gökmen Tayfur
4. Vijay P. Singh
5. Ozgur Kisi
6. Demetris Koutsoyiannis
7. Simon Michael Papalexiou

Citations in the corpus

• Estimation of longitudinal dispersion coefficient in rivers (2014, methodology): “The discrepancy ratio (DR), which is an error measure commonly used in the referred literature (Kashefipour and Falconer, 2002; Tayfur and Singh, 2005), was adopted to evaluate the prediction precision of different formulae,”
• ANN and Fuzzy Logic Models for Simulating Event-Based Rainfall-Runoff (2006, methodology): “ANN has also been applied to sediment transport Tayfur 2002b ; streamflow forecasting Wu et al. 2005; Anctil and Rat 2005 ; rainfall forecasting Ramírez et al. 2005; Olsson et al. 2004 ; seepage Tayfur et al. 2005 ; and dispersion Tayfur and Singh 2005 .”
• Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration (2010, background): “95% confidence intervals for the estimates of daily CO con Deng et al. (2001), Kashefipour and Falconer (2002), Tayfur and Singh (2005).”