Is it possible developing reliable prediction models considering only the pipe's age for decision-making in sewer asset management?. (5th May 2021)

• Record Type: Journal Article
• Authors: Hernandez, Nathalie; Caradot, Nicolas; Sonnenberg, Hauke; Rouault, Pascale; Torres, Andrés
• Is Part Of: Journal of modelling in management. Volume 16:Number 4(2021)
• Page Start: 1166
• Page End: 1184
• Publication Date: 2021-05-05
• Subjects: Planning -- Artificial intelligence -- Environmental management -- Data mining -- Replacement -- Environmental research -- Sewer asset management -- Logistic regression models -- Random Forest models -- Linear discriminant analysis models -- Support vector machines models -- Proactive management; Industrial management -- Mathematical models -- Periodicals; Industrial management -- Computer simulation -- Periodicals; Business -- Mathematical models -- Periodicals; Business -- Computer simulation -- Periodicals; 658.4033
• DOI: 10.1108/JM2-11-2019-0258
• Languages: English
• ISSNs: 1746-5664

Abstract

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.