Next Article in Journal
Spatiotemporal Evolution and the Influencing Factors of Tourism-Based Social-Ecological System Vulnerability in the Three Gorges Reservoir Area, China
Next Article in Special Issue
Implementing Water-Energy-Land-Food-Climate Nexus Approach to Achieve the Sustainable Development Goals in Greece: Indicators and Policy Recommendations
Previous Article in Journal
The Impact of Air Quality on Effective Labor Supply: Based on the Survey Data of Zhejiang Province in China
Previous Article in Special Issue
Clean Energies for Ghana—An Empirical Study on the Level of Social Acceptance of Renewable Energy Development and Utilization

Sediment Level Prediction of a Combined Sewer System Using Spatial Features

Eurecat, Technology Centre of Catalonia, 08005 Barcelona, Spain
Department of Computer Science and Industrial Engineering, University of Lleida, 25003 Lleida, Spain
Barcelona Cicle de l’Aigua, 08038 Barcelona, Spain
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;
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
Show Figures

Figure 1

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.

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.

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.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop