Predicting discharge coefficient of compound broad-crested weir by using genetic programming (GP) and artificial neural network (ANN) techniques

By F. Salmasi, Gürol Yildirim, A. Masoodi, P. Parsamehr. Arabian Journal of Geosciences, 2013

48 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Soil Science

View Paper: https://www.semanticscholar.org/paper/018d1191a25e64b700e99867d15c4fd5c1e2c8cc

How researchers see this paper

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

1. M. Akbari
2. F. Salmasi
3. H. Arvanaghi
4. Masoud Karbasi
5. D. Farsadizadeh

Citations in the corpus

• Application of Gaussian Process Regression Model to Predict Discharge Coefficient of Gated Piano Key Weir (2019, methodology): “Salmasi et al. (2012) estimated the discharge coefficient of compound broad-crested weirs by applying the Genetic Programming (GP) techniques and ANN.”