@inproceedings{scholars5295,
         address = {Ostend},
           title = {Estimation of soil pore-water pressure variations using a thin plate spline basis function},
          volume = {137},
            note = {cited By 1; Conference of International Conference on High Performance and Optimum Design of Structures and Materials, HPSM/OPTI 2014 ; Conference Date: 9 June 2014 Through 11 June 2014; Conference Code:105905},
             doi = {10.2495/HPSM140561},
         journal = {WIT Transactions on the Built Environment},
       publisher = {WITPress},
           pages = {615--624},
            year = {2014},
            isbn = {9781845647742},
          author = {Mustafa, M. R. and Rezaur, R. B. and Isa, M. H. and Rahardjo, H.},
            issn = {17433509},
        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. {\^A}{\copyright} 2014 WIT Press.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84903168941&doi=10.2495\%2fHPSM140561&partnerID=40&md5=89007571f23bf5194f0f32625155df96},
        keywords = {Forecasting; Functions; Mean square error; Network architecture; Neural networks; Rain; Soils; Structural optimization; Time series, Coefficient of correlation; Coefficient of efficiencies; Number of hidden neurons; Pore-water pressures; Radial basis function neural networks; Radial basis functions; Thin plate spline; Trial-and-error procedures, Radial basis function networks, artificial neural network; climate effect; error analysis; numerical model; performance assessment; porewater; soil water; time series}
}