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Application of Artificial Neural Network and Genetic Programming in Civil Engineering
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Author(s): Pijush Samui (VIT University, India), Dhruvan Choubisa (VIT University, India)and Akash Sharda (VIT University, India)
Copyright: 2014
Pages: 17
Source title:
Biologically-Inspired Techniques for Knowledge Discovery and Data Mining
Source Author(s)/Editor(s): Shafiq Alam (University of Auckland, New Zealand), Gillian Dobbie (University of Auckland, New Zealand), Yun Sing Koh (University of Auckland, New Zealand)and Saeed ur Rehman (Unitec Institute of Technology, New Zealand)
DOI: 10.4018/978-1-4666-6078-6.ch009
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Abstract
This chapter examines the capability of Genetic Programming (GP) and different Artificial Neural Network (ANN) (Backpropagation [BP] and Generalized Regression Neural Network [GRNN]) models for prediction of air entrainment rate (QA) of triangular sharp-crested weir. The basic principal of GP has been taken from the concept of Genetic Algorithm (GA). Discharge (Q), drop height (h), and angle in triangular sharp-crested weir (?) are considered as inputs of BP, GRNN, and GP. Coefficient of Correlation (R) has been used to assess the performance of developed GP, BP, and GRNN models. For a perfect model, the value of R should be close to one. A sensitivity analysis has been carried out to determine the effect of each input parameter. This chapter presents a comparative study between the developed BP, GRNN, and GP models.
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