Kouzehkalani Sales Ali, Gul Enes, Safari Mir Jafar Sadegh
Department of Civil Engineering, Elm-O-Fan University College of Science and Technology, Urmia, Iran.
Department of Civil Engineering, Inonu University, Malatya, Turkey.
Environ Sci Pollut Res Int. 2023 Mar;30(14):39637-39652. doi: 10.1007/s11356-022-24989-0. Epub 2023 Jan 4.
Sediment transport is a noteworthy task in the design and operation of sewer pipes. Decreasing sewer pipe hydraulic capacity and transport of pollution are the main consequences of continuous sedimentation. Among different design approaches, the non-deposition with deposited bed (NDB) method can be used for the design of large sewer pipes; however, existing models are established on limited data ranges and mostly applied conventional regression methods. The current study improves the NDB sediment transport modeling by utilizing wide data ranges, and furthermore, applying robust machine learning techniques. In the present study, the conventional extreme learning machine (ELM) technique and its advanced versions, namely the online sequential-extreme learning machine (OS-ELM), outlier robust-extreme learning machine (OR-ELM), and parallel layer perceptron-extreme learning machine (PLP-ELM) are used for the modeling. In the studies conducted in the literature, sediment deposited bed thickness (t) or deposited bed width (W) was used in the model structure as a deposited sediment variable, and therefore, different parameters in terms of t and W can be incorporated into the model structure. However, an uncertainty arises in the selection of the appropriate parameter among W/Y, t/Y, W/D, and t/D (Y is flow depth and D circular pipe diameter). In order to define the most appropriate parameter to best describe the impact of deposited sediment at the channel bottom in the modeling procedure, four various scenarios using four different parameters that incorporate deposited sediment variables at their structures as W/Y, t/Y, W/D, and t/D are considered for model development. It is found that models that incorporate sediment bed thickness (t) provide better results than those which use deposited bed width (W) in their structures. Among four different scenarios, models that utilized t/D dimensionless parameter, give superior results in contrast to their alternatives. Based on the outcomes, the OR-ELM approach outperformed ELM, OS-ELM, and PLP-ELM techniques. The results obtained from applied methods are compared to their corresponding models in the literature, indicating the superiority of the OR-ELM model. It is figured out that the thickness of the deposited bed is an effective variable in modeling NDB sediment transport in sewer pipes.
在下水道管道的设计和运行中，沉积物输送是一项值得关注的任务。下水道管道水力容量的降低和污染物的输送是持续沉积的主要后果。在不同的设计方法中，有沉积床的不沉积（NDB）方法可用于大型下水道管道的设计；然而，现有的模型是基于有限的数据范围建立的，并且大多应用传统的回归方法。当前的研究通过利用广泛的数据范围并进一步应用强大的机器学习技术，改进了NDB沉积物输送建模。在本研究中，使用传统的极限学习机（ELM）技术及其高级版本，即在线序贯极限学习机（OS - ELM）、离群稳健极限学习机（OR - ELM）和平行层感知器极限学习机（PLP - ELM）进行建模。在文献中进行的研究中，沉积物沉积床厚度（t）或沉积床宽度（W）在模型结构中被用作沉积沉积物变量，因此，关于t和W的不同参数可以纳入模型结构。然而，在W/Y、t/Y、W/D和t/D（Y是水流深度，D是圆形管道直径）中选择合适的参数时会出现不确定性。为了在建模过程中确定最能描述渠道底部沉积沉积物影响的最合适参数，考虑了四种不同的情况，在其结构中使用四个不同的参数将沉积沉积物变量纳入其中，分别为W/Y、t/Y、W/D和t/D来进行模型开发。结果发现，包含沉积物床厚度（t）的模型比在其结构中使用沉积床宽度（W）的模型提供了更好的结果。在四种不同的情况中，使用t/D无量纲参数的模型与其替代方案相比给出了更优的结果。基于这些结果，OR - ELM方法优于ELM、OS - ELM和PLP - ELM技术。将应用方法获得的结果与其在文献中的相应模型进行比较，表明了OR - ELM模型的优越性。结果表明，沉积床的厚度是下水道管道中NDB沉积物输送建模的一个有效变量。
