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

Carlos Montes, Zoran Kapelan, Juan Saldarriaga

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Article number116639
Pages (from-to)1-11
Number of pages11
JournalWater Research
Volume189
DOIs
Publication statusPublished - 2021

Keywords

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

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