Department of Civil and Environmental Engineering, Universidad de los Andes, Bogotá, Colombia.
Department of Water Management, Delft University of Technology, Delft, Netherlands.
Water Res. 2021 Feb 1;189:116639. doi: 10.1016/j.watres.2020.116639. Epub 2020 Nov 13.
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.
过去已经对下水道中的泥沙输送进行了广泛的研究。本文旨在提出一种新的方法来预测避免下水道管道中物质永久沉积所需的自清洁速度。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。使用多个观测数据集评估并比较了所开发模型与十个有前途的文献模型的准确性。结果表明，RF 模型能够对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的预测，特别是在用于设计大型下水道的沉积床标准的非沉积情况下。体积泥沙浓度被确定为预测自清洁速度的最重要参数。
