TY - GEN
T1 - Support tools to predict the critical structural condition of uninspected sewer pipes in Bogota DC
AU - Torres Abello, Andres Eduardo
AU - Hernández, Nathalie
AU - Caradot, Nicolas
AU - Sonnenberg, Hauke
AU - Rouault, Pascale
PY - 2017
Y1 - 2017
N2 - The objective of this work is to explore five methods based on learning machine and statistical approaches (logistic regression –LR–, random forest –RF–, multinomial logistic regression –Multi_LR, lineal discriminant analysis –LDA– and support vector machine –SVM–) considering: (i) only the age as independent input variable and (ii) other sewer characteristics together with age as input variables in the models in order to establish which one is the most adapted as sewer asset management support tool for CCTV dataset from Bogota. From ROC space and performance curves techniques, it is possible to establish that LR and RF methods are suitable to predict the structural condition of the uninspected sewer pipes and to identify pipes in critical conditions, considering more than one variable as the independent input variables in the models.
AB - The objective of this work is to explore five methods based on learning machine and statistical approaches (logistic regression –LR–, random forest –RF–, multinomial logistic regression –Multi_LR, lineal discriminant analysis –LDA– and support vector machine –SVM–) considering: (i) only the age as independent input variable and (ii) other sewer characteristics together with age as input variables in the models in order to establish which one is the most adapted as sewer asset management support tool for CCTV dataset from Bogota. From ROC space and performance curves techniques, it is possible to establish that LR and RF methods are suitable to predict the structural condition of the uninspected sewer pipes and to identify pipes in critical conditions, considering more than one variable as the independent input variables in the models.
KW - sewer asset management
KW - performance curve
KW - ROC space
KW - machine learning methods
KW - proactive management
M3 - Conference contribution
BT - The leading edge sustainable asset management of water and wastewater infrastructure conference
ER -