Sediment Level Prediction of a Combined Sewer System Using Spatial Features

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2021Author
Ribalta, Marc
Rubión Soler, Edgar
Echeverria, Lluís
Varela Alegre, Francisco Javier
Corominas, Lluís
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Ribalta, Marc;
Mateu Piñol, Carles;
Béjar Torres, Ramón;
Rubión Soler, Edgar;
Echeverria, Lluís;
Varela Alegre, Francisco Javier;
Corominas, Lluís;
.
(2021)
.
Sediment Level Prediction of a Combined Sewer System Using Spatial Features.
Sustainability, 2021, vol. 13, núm. 7, 4013.
https://doi.org/10.3390/su13074013.
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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.
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Sustainability, 2021, vol. 13, núm. 7, 4013European research projects
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