Practical benchmarking of statistical and machine learning models for predicting the condition of sewer pipes in Berlin, Germany

• Published in: Journal of hydroinformatics Vol. 20; no. 5; pp. 1131 - 1147
• Main Authors: Caradot, N., Riechel, M., Fesneau, M., Hernandez, N., Torres, A., Sonnenberg, H., Eckert, E., Lengemann, N., Waschnewski, J., Rouault, P.
• Format: Journal Article
• Language: English
• Published: London IWA Publishing 01.09.2018
• Subjects: Artificial intelligence; Asset management; Decision trees; Deterioration; Forests; Infrastructure; Inspection; Learning algorithms; Machine learning; Markov chains; Mathematical models; Modelling; Performance assessment; Pipes; Rehabilitation; Sewer pipes; Sewer systems; Statistical analysis; Statistical models; Survival; Survival analysis; United States > US; Berlin Germany; Germany

Deterioration models can be successfully deployed only if decision-makers trust the modelling outcomes and are aware of model uncertainties. Our study aims to address this issue by developing a set of clearly understandable metrics to assess the performance of sewer deterioration models from an end-...