Evaluation of low degree polynomial kernel support vector machines for modelling Pore-water pressure responses

Abstract

Abstract. Pore-water pressure (PWP) is influenced by climatic changes, especially rainfall. These changes may affect the stability of, particularly unsaturated slopes. Thus monitoring the changes in PWP resulting from climatic factors has become an important part of effective slope management. However, this monitoring requires field instrumentation program, which is resource and labour expensive. Recently, soft computing modelling has become an alternative. Low degree polynomial kernel support vector machine (SVM) was evaluated in modelling the PWP changes. The developed model used pore-water pressure and rainfall data collected from an instrumented slope. Wrapper technique was used to select input features and k-fold cross validation was used to calibrate the model parameters. The developed model showed great promise in modelling the pore-water pressure changes. High correlation, with coefficient of determination of 0.9694 between the predicted and observed changes was obtained. The one degree polynomial SVM model yielded competitive result, and can be used to provide lead time records of PWP which can aid in better slope management.

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“…Tuning parameter values cannot be calculated analytically, so in soil science applications, grid search is often used as a standard technique (e.g. Babangida et al, 2016;Khlosi et al, 2016;Twarakavi et al, 2009). It works by testing a number of predefined parameter values or combinations of parameter values to finally choose the best.…”