TY - JOUR
T1 - Evaluation of low degree polynomial kernel support vector machines for modelling Pore-water pressure responses
AU - Babangida, Nuraddeen Muhammad
AU - Ul Mustafa, Muhammad Raza
AU - Yusuf, Khamaruzaman Wan
AU - Isa, Mohamed Hasnain
AU - Baig, Imran
N1 - Publisher Copyright:
© The Authors.
PY - 2016/5/24
Y1 - 2016/5/24
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85009399772&partnerID=8YFLogxK
U2 - 10.1051/matecconf/20165904003
DO - 10.1051/matecconf/20165904003
M3 - Conference article
AN - SCOPUS:85009399772
SN - 2261-236X
VL - 59
JO - MATEC Web of Conferences
JF - MATEC Web of Conferences
M1 - 04003
T2 - 2016 International Conference on Frontiers of Sensors Technologies, ICFST 2016
Y2 - 12 March 2016 through 14 March 2016
ER -