A bilevel data-driven method for sewer deposit prediction under uncertainty

Wenli Liu; Yexin He; Zihan Liu; Hanbin Luo; Tianxiang Liu

doi:10.1016/j.watres.2023.119588

A bilevel data-driven method for sewer deposit prediction under uncertainty

Water Res. 2023 Mar 1:231:119588. doi: 10.1016/j.watres.2023.119588. Epub 2023 Jan 11.

Authors

Wenli Liu¹, Yexin He², Zihan Liu³, Hanbin Luo⁴, Tianxiang Liu⁵

Affiliations

¹ Lecturer, Dept. of Construction Management, School of Civil and hydraulic Engineering, Huazhong University of Science and Technology, Wuhan Hubei 430074, China. Electronic address: liu_wenli@hust.edu.cn.
² Master candidate, Dept. of Construction Management, School of Civil and hydraulic Engineering, Huazhong University of Science & Technology, Wuhan Hubei 430074, China. Electronic address: patick_@hust.edu.cn.
³ Doctor candidate, Dept. of Construction Management, School of Civil and hydraulic Engineering, Huazhong University of Science and Technology, Wuhan Hubei 430074, China. Electronic address: liuzihan1996@hust.edu.cn.
⁴ Professor, Dept. of Construction Management, School of Civil and hydraulic Engineering, Huazhong University of Science and Technology, Wuhan Hubei 430074, China. Electronic address: luohbcem@hust.edu.cn.
⁵ Master candidate, Dept. of Construction Management, School of Civil and hydraulic Engineering, Huazhong University of Science & Technology, Wuhan Hubei 430074, China. Electronic address: m202171531@hust.edu.cn.

PMID: 36680829
DOI: 10.1016/j.watres.2023.119588

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.

Keywords: Generalized linear mixed modeling (GLMM); Global sensitivity analysis (GSA); Polynomial-Chaos Kriging (PC-Kriging); Sewer deposits; Sewer system.

MeSH terms

Models, Theoretical*
Sewage* / analysis
Uncertainty

Substances

Sewage