Prediction of pore-water pressure using radial basis function neural network

Knowledge of soil pore-water pressure variation due to climatic changes is fundamental for slope stability analysis and other problems associated with slope stability issues. This study is an application of Radial Basis Function Neural Network (RBFNN) modeling for prediction of soil pore-water press...

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Main Authors: Mustafa, M.R., Rezaur, R.B., Rahardjo, H., Isa, M.H.
Format: Citation Index Journal
Published: 2012
Subjects:
Online Access:http://eprints.utp.edu.my/10772/1/Prediction%20of%20pore-water%20pressure%20using%20radial%20basis%20function%20neural%20network.pdf
http://eprints.utp.edu.my/10772/
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spelling my.utp.eprints.107722013-12-16T23:48:30Z Prediction of pore-water pressure using radial basis function neural network Mustafa, M.R. Rezaur, R.B. Rahardjo, H. Isa, M.H. TC Hydraulic engineering. Ocean engineering Knowledge of soil pore-water pressure variation due to climatic changes is fundamental for slope stability analysis and other problems associated with slope stability issues. This study is an application of Radial Basis Function Neural Network (RBFNN) modeling for prediction of soil pore-water pressure responses to rainfall. Time series data of rainfall and pore-water pressures were used to develop the RBFNN prediction model. The number of input neurons was decided by the analysis of auto-correlation between pore-water pressure data and cross-correlation between rainfall and pore-water pressure data. Establishing the number of hidden neurons by method of self learning network architecture determination and also by trial and error method was examined. A number of statistical measures were used for the evaluation of the network performance. Prediction results with a network architecture of 8–10–1 and a spread σ=3.0 produced the lowest error measures (MSE, RMSE, MAE), highest coefficient of efficiency (CE) and coefficient of determination (R2). The results suggest that RBFNN is suitable for mapping the non-linear, complex behavior of porewater pressure responses to rainfall. Guidelines for choosing the number of input neurons and eliminating possibility of model over-fitting are also discussed. 2012-05 Citation Index Journal PeerReviewed application/pdf http://eprints.utp.edu.my/10772/1/Prediction%20of%20pore-water%20pressure%20using%20radial%20basis%20function%20neural%20network.pdf Mustafa, M.R. and Rezaur, R.B. and Rahardjo, H. and Isa, M.H. (2012) Prediction of pore-water pressure using radial basis function neural network. [Citation Index Journal] http://eprints.utp.edu.my/10772/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
topic TC Hydraulic engineering. Ocean engineering
spellingShingle TC Hydraulic engineering. Ocean engineering
Mustafa, M.R.
Rezaur, R.B.
Rahardjo, H.
Isa, M.H.
Prediction of pore-water pressure using radial basis function neural network
description Knowledge of soil pore-water pressure variation due to climatic changes is fundamental for slope stability analysis and other problems associated with slope stability issues. This study is an application of Radial Basis Function Neural Network (RBFNN) modeling for prediction of soil pore-water pressure responses to rainfall. Time series data of rainfall and pore-water pressures were used to develop the RBFNN prediction model. The number of input neurons was decided by the analysis of auto-correlation between pore-water pressure data and cross-correlation between rainfall and pore-water pressure data. Establishing the number of hidden neurons by method of self learning network architecture determination and also by trial and error method was examined. A number of statistical measures were used for the evaluation of the network performance. Prediction results with a network architecture of 8–10–1 and a spread σ=3.0 produced the lowest error measures (MSE, RMSE, MAE), highest coefficient of efficiency (CE) and coefficient of determination (R2). The results suggest that RBFNN is suitable for mapping the non-linear, complex behavior of porewater pressure responses to rainfall. Guidelines for choosing the number of input neurons and eliminating possibility of model over-fitting are also discussed.
format Citation Index Journal
author Mustafa, M.R.
Rezaur, R.B.
Rahardjo, H.
Isa, M.H.
author_facet Mustafa, M.R.
Rezaur, R.B.
Rahardjo, H.
Isa, M.H.
author_sort Mustafa, M.R.
title Prediction of pore-water pressure using radial basis function neural network
title_short Prediction of pore-water pressure using radial basis function neural network
title_full Prediction of pore-water pressure using radial basis function neural network
title_fullStr Prediction of pore-water pressure using radial basis function neural network
title_full_unstemmed Prediction of pore-water pressure using radial basis function neural network
title_sort prediction of pore-water pressure using radial basis function neural network
publishDate 2012
url http://eprints.utp.edu.my/10772/1/Prediction%20of%20pore-water%20pressure%20using%20radial%20basis%20function%20neural%20network.pdf
http://eprints.utp.edu.my/10772/
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score 13.211869