dc.contributor.authorMustafa, M. R.
dc.contributor.authorRezaur, R. B.
dc.contributor.authorRahardjo, Harianto
dc.contributor.authorIsa, M. H.
dc.date.accessioned2013-07-11T03:54:02Z
dc.date.available2013-07-11T03:54:02Z
dc.date.copyright2012en_US
dc.date.issued2012
dc.identifier.citationMustafa, M. R., Rezaur, R. B., Rahardjo, H., Isa, M. H. (2012). Prediction of pore-water pressure using radial basis function neural network. Engineering geology, 135-136, 40-47.
dc.identifier.urihttp://hdl.handle.net/10220/11185
dc.description.abstractKnowledge 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 pore-water pressure responses to rainfall. Guidelines for choosing the number of input neurons and eliminating possibility of model over-fitting are also discussed.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesEngineering geologyen_US
dc.rights© 2012 Elsevier B.V.en_US
dc.titlePrediction of pore-water pressure using radial basis function neural networken_US
dc.typeJournal Article
dc.contributor.schoolSchool of Civil and Environmental Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1016/j.enggeo.2012.02.008


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