Is it possible developing reliable prediction models considering only the pipe’s age for decision-making in sewer asset management?
Journal of Modelling in Management
ISSN: 1746-5664
Article publication date: 5 May 2021
Issue publication date: 25 November 2021
Abstract
Purpose
The purpose of this paper was exploring and comparing different deterioration models based on statistical and machine learning approaches. These models were chosen from their successful results in other case studies. The deterioration models were developing considering two scenarios: (i) only the age as covariate (Scenario 1); and (ii) the age together with other available sewer characteristics as covariates (Scenario 2). Both were evaluated to achieve two different management objectives related to the prediction of the critical condition of sewers: at the network and the sewer levels.
Design/methodology/approach
Six statistical and machine learning methods [logistic regression (LR), random forest (RF), multinomial logistic regression, ordinal logistic regression, linear discriminant analysis and support vector machine] were explored considering two kinds of predictor variables (independent variables in the model). The main propose of these models was predicting the structural condition at network and pipe level evaluated from deviation analysis and performance curve techniques. Further, the deterioration models were exploring for two case studies: the sewer systems of Bogota and Medellin. These case studies were considered because of both counts with their own assessment standards and low inspection rate.
Findings
The results indicate that LR models for both case studies show higher prediction capacity under Scenario 1 (considering only the age) for the management objective related to the network, such as annual budget plans; and RF shows the highest success percentage of sewers in critical condition (sewer level) considering Scenario 2 for both case studies.
Practical implications
There is not a deterioration method whose predictions are adaptable for achieving different management objectives; it is important to explore different approaches to find which one could support a sewer asset management objective for a specific case study.
Originality/value
The originality of this paper consists of there is not a paper in which the prediction of several statistical and machine learning-based deterioration models has been compared for case studies with different local assessment standard. The above to find which is adaptable for each one and which model is adaptable for each management objective.
Keywords
Acknowledgements
This research is supported by the program PROCOL, bilateral convention between DAAD in Germany (Proposal title: “Development of innovative tools to support efficient sewer asset management strategies in Germany and Colombia) and COLCIENCIAS in Colombia (Colciencias – Pontificia Universidad Javeriana “Contrato de Financiamiento de Recuperación Contingente de Movilidad Internacional No. 646 del 2015” and Project ID: 6725 - PRY ID – 6853 - Proposal title: “Herramientas de gestión proactiva de alcantarillados adapatadas a diferentes contextos de gestión patrimonial”) including funding from the German Federal Ministry of Education and Research (BMBF).
Furthermore, the authors would like to thank COLCIENCIAS and PUJ for supporting one of the authors in her PhD studies (“Convocatoria 727 del 2015 – Apoyo doctorados nacionales”).
A special acknowledgement is given to Empresa de Acueducto de Bogota (EAB) for supplying the database information used in this research.
Citation
Hernandez, N., Caradot, N., Sonnenberg, H., Rouault, P. and Torres, A. (2021), "Is it possible developing reliable prediction models considering only the pipe’s age for decision-making in sewer asset management?", Journal of Modelling in Management, Vol. 16 No. 4, pp. 1166-1184. https://doi.org/10.1108/JM2-11-2019-0258
Publisher
:Emerald Publishing Limited
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