Sediment Level Prediction of a Combined Sewer System Using Spatial Features

Marc Ribalta, Carles Mateu, Ramon Bejar, Edgar Rubión, Lluís Echeverria, Francisco Javier Varela Alegre and Lluís Corominas
Additional contact information
Marc Ribalta: Eurecat, Technology Centre of Catalonia, 08005 Barcelona, Spain
Carles Mateu: Department of Computer Science and Industrial Engineering, University of Lleida, 25003 Lleida, Spain
Ramon Bejar: Department of Computer Science and Industrial Engineering, University of Lleida, 25003 Lleida, Spain
Edgar Rubión: Eurecat, Technology Centre of Catalonia, 08005 Barcelona, Spain
Lluís Echeverria: Eurecat, Technology Centre of Catalonia, 08005 Barcelona, Spain
Francisco Javier Varela Alegre: Barcelona Cicle de l’Aigua, 08038 Barcelona, Spain
Lluís Corominas: ICRA, Catalan Institute for Water Research, 17003 Girona, Spain

Sustainability, 2021, vol. 13, issue 7, 1-15

Abstract: The prediction of sediment levels in combined sewer system (CSS) would result in enormous savings in resources for their maintenance as a reduced number of inspections would be needed. In this paper, we benchmark different machine learning (ML) methodologies to improve the maintenance schedules of the sewerage and reduce the number of cleanings using historical sediment level and inspection data of the combined sewer system in the city of Barcelona. Two ML methodologies involve the use of spatial features for sediment prediction at critical sections of the sewer, where the cost of maintenance is high because of the dangerous access; one uses a regression model to predict the sediment level of a section, and the other one a binary classification model to identify whether or not a section needs cleaning. The last ML methodology is a short-term forecast of the possible sediment level in future days to improve the ability of operators to react and solve an imminent sediment level increase. Our study concludes with three different models. The spatial and short-term regression methodologies accomplished the best results with Artificial Neural Networks (ANN) with 0.76 and 0.61 R2 scores, respectively. The classification methodology resulted in a Gradient Boosting (GB) model with an accuracy score of 0.88 and an area under the curve (AUC) of 0.909.

Keywords: predictive maintenance; sewer blockages; wastewater; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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