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Article

Sediment Level Prediction of a Combined Sewer System Using Spatial Features

1
Eurecat, Technology Centre of Catalonia, 08005 Barcelona, Spain
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Department of Computer Science and Industrial Engineering, University of Lleida, 25003 Lleida, Spain
3
Barcelona Cicle de l’Aigua, 08038 Barcelona, Spain
4
ICRA, Catalan Institute for Water Research, 17003 Girona, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Maria P. Papadopoulou, Chrysaida-Aliki Papadopoulou and Chrysi S. Laspidou
Sustainability 2021, 13(7), 4013; https://doi.org/10.3390/su13074013
Received: 24 January 2021 / Revised: 21 March 2021 / Accepted: 1 April 2021 / Published: 3 April 2021
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. View Full-Text
Keywords: predictive maintenance; sewer blockages; wastewater; machine learning predictive maintenance; sewer blockages; wastewater; machine learning
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MDPI and ACS Style

Ribalta, M.; Mateu, C.; Bejar, R.; Rubión, E.; Echeverria, L.; Varela Alegre, F.J.; Corominas, L. Sediment Level Prediction of a Combined Sewer System Using Spatial Features. Sustainability 2021, 13, 4013. https://doi.org/10.3390/su13074013

AMA Style

Ribalta M, Mateu C, Bejar R, Rubión E, Echeverria L, Varela Alegre FJ, Corominas L. Sediment Level Prediction of a Combined Sewer System Using Spatial Features. Sustainability. 2021; 13(7):4013. https://doi.org/10.3390/su13074013

Chicago/Turabian Style

Ribalta, Marc, Carles Mateu, Ramon Bejar, Edgar Rubión, Lluís Echeverria, Francisco Javier Varela Alegre, and Lluís Corominas. 2021. "Sediment Level Prediction of a Combined Sewer System Using Spatial Features" Sustainability 13, no. 7: 4013. https://doi.org/10.3390/su13074013

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