Sediment Level Prediction of a Combined Sewer System Using Spatial Features

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

▼ Show Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share and Cite

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

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 Metrics

Abstract views Pdf views Html views

Article Menu

Sediment Level Prediction of a Combined Sewer System Using Spatial Features

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Article Access Map by Country/Region

Further Information

Guidelines

MDPI Initiatives

Follow MDPI