Estimation Of Soil Pore-water Pressure
Variations Using A Thin Plate Spline
Basis Function
Author(s)
M. R. Mustafa, R. B. Rezaur, M. H. Isa & H. Rahardjo
Abstract
Information of soil pore-water pressure changes due to climatic effect is an
integral part for studies associated with hill slope analysis. Soil pore-water
pressure variations in a soil slope due to rainfall were predicted using Artificial
Neural Network (ANN) technique with Thin Plate Spline (TPS) radial basis
function. A radial basis function (RBF) neural network with network architecture
of 8-36-1 (input-hidden-output) was selected to develop RBF model. Number of
hidden neurons was selected using trial and error procedure whereas spread of
the basis function was established using normalization method. Time series data
of rainfall and pore-water pressure was used for training and testing the RBF
model. The performance of the model was evaluated using root mean square
error, coefficient of correlation and coefficient of efficiency. The results of the
model prediction revealed that the model produced promising results indicating
that TPS basis function is able to predict time series of pore-water pressure
responses to rainfall. Comparison with other studies showed that the RBF model
using TPS basis function can be used as alternate of Gaussian basis function for
prediction of soil pore-water pressure variations.
Keywords: neural network, pore-water pressure, prediction, thin plate spline,
radial basis function, rainfall.
Keywords
neural network, pore-water pressure, prediction, thin plate spline,
radial basis function, rainfall.
Related Book
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