Predicting non-deposition sediment transport in sewer pipes using Random forest

Montes, Carlos; Kapelan, Z.; Saldarriaga, Juan

doi:10.1016/j.watres.2020.116639

Predicting non-deposition sediment transport in sewer pipes using Random forest

Title

Predicting non-deposition sediment transport in sewer pipes using Random forest

Author

Montes, Carlos (Universidad de los Andes)
Kapelan, Z. (TU Delft Sanitary Engineering)
Saldarriaga, Juan (Universidad de los Andes)

Date

2021

Abstract

Sediment transport in sewers has been extensively studied in the past. This paper aims to propose a new method for predicting the self-cleansing velocity required to avoid permanent deposition of material in sewer pipes. The new Random Forest (RF) based model was implemented using experimental data collected from the literature. The accuracy of the developed model was evaluated and compared with ten promising literature models using multiple observed datasets. The results obtained demonstrate that the RF model is able to make predictions with high accuracy for the whole dataset used. These predictions clearly outperform predictions made by other models, especially for the case of non-deposition with deposited bed criterion that is used for designing large sewer pipes. The volumetric sediment concentration was identified as the most important parameter for predicting self-cleansing velocity.

Subject

Non-deposition
Random forest
Sediment transport
Self-cleansing
Sewer systems

To reference this document use:

http://resolver.tudelft.nl/uuid:b08fc5dc-06df-4ce8-a3ec-196af9508a4a

DOI

https://doi.org/10.1016/j.watres.2020.116639

Embargo date

2022-11-20

ISSN

0043-1354

Source

Water Research, 189, 1-11