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.

过去已经广泛研究了下水道中的泥沙输送。本文旨在提出一种新的预测自清洁速度的方法，该速度可避免污水管道中物质的永久沉积。新的基于随机森林（RF）的模型是使用从文献中收集的实验数据实现的。评价了开发模型的准确性，并使用多个观察到的数据集与十个有前途的文献模型进行了比较。获得的结果表明，RF模型能够对所使用的整个数据集进行高精度的预测。这些预测明显优于其他模型所做的预测，特别是对于用于设计大型下水道的沉积床标准非沉积的情况。