A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Sewer Condition Prediction and Analysis of Explanatory Factors
Sewer condition is commonly assessed using closed-circuit television (CCTV) inspections. In this paper, we combine inspection results, pipe attributes, network data, and data on pipe environment to predict pipe condition and to discover which factors affect it. We apply the random forest algorithm to model pipe condition and assess the variable importance using the Boruta algorithm. We analyse the impact of predictor variables on poor condition using partial dependence plots, which are a valuable technique for this purpose. The results can be used in screening pipes for future inspections and provide insight into the dynamics between predictor variables and poor condition.
Sewer Condition Prediction and Analysis of Explanatory Factors
Sewer condition is commonly assessed using closed-circuit television (CCTV) inspections. In this paper, we combine inspection results, pipe attributes, network data, and data on pipe environment to predict pipe condition and to discover which factors affect it. We apply the random forest algorithm to model pipe condition and assess the variable importance using the Boruta algorithm. We analyse the impact of predictor variables on poor condition using partial dependence plots, which are a valuable technique for this purpose. The results can be used in screening pipes for future inspections and provide insight into the dynamics between predictor variables and poor condition.
Sewer Condition Prediction and Analysis of Explanatory Factors
Tuija Laakso (author) / Teemu Kokkonen (author) / Ilkka Mellin (author) / Riku Vahala (author)
2018
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Condition Prediction of Sanitary Sewer Pipes
| TIBKAT | 2019
Integrating Sewer Condition with Sewer Management
| British Library Conference Proceedings | 1999
Sewer Pipeline Operational Condition Prediction using Multiple Regression
| British Library Conference Proceedings | 2007
Infrastructure Condition Prediction Models for Sustainable Sewer Pipelines
| Online Contents | 2008