A bilevel data-driven method for sewer deposit prediction under uncertainty. (1st March 2023) - Liu, Wenli; He, Yexin; Liu, Zihan; Luo, Hanbin; Liu, Tianxiang
Highlights: A bilevel data-driven method is proposed to predict sewer deposits in urban areas. The catchment-level parameter, LCSOO, is generated by GLMM and presented by GIS. The deposit prediction model based on PC-Kriging achieves optimal model performance. Three GSA methods are utilized to recognize influential parameters for rapid deposit prediction. Abstract: Deposit accumulation is one of the predominant causes of sewer blockage and overflow. Nevertheless, the traditional detection methods are costly and time-consuming, and the accuracy of the mathematical models for deposit prediction is usually affected by some uncertain factors (e.g., pipe properties and flow velocity of water). This paper proposes a framework of global sensitivity analysis (GSA) to identify the most sensitive indicators for sewer deposit prediction by (i) developing a data-driven bilevel (i.e., catchment level and segment level) model to map the relation between input and output indicators and (ii) employing three different GSA methods, namely, the Morris method, Sobol method, and Borgonovo index method to identify the indicators as important or unimportant (insensitive). The results show that the likelihood of combined sewer overflow occurrences (LCSOO), pipe age (PA), and pipe material (PM) are influential parameters for the thickness of deposits. Here, we pay close attention to the most influential parameters, which can help improve forecast prediction accuracy. Graphical abstract: Image, graphical abstract