BK-SWMM flood simulation framework is being proposed for urban storm flood modeling based on uncertainty parameter crowdsourcing data from a single functional region

Chengshuai Liu; Wenzhong Li; Chenchen Zhao; Tianning Xie; Shengqi Jian; Qiang Wu; Yingying Xu; Caihong Hu

doi:10.1016/j.jenvman.2023.118482

BK-SWMM flood simulation framework is being proposed for urban storm flood modeling based on uncertainty parameter crowdsourcing data from a single functional region

J Environ Manage. 2023 Oct 15:344:118482. doi: 10.1016/j.jenvman.2023.118482. Epub 2023 Jul 5.

Authors

Chengshuai Liu¹, Wenzhong Li², Chenchen Zhao², Tianning Xie², Shengqi Jian², Qiang Wu³, Yingying Xu², Caihong Hu⁴

Affiliations

¹ Yellow River Laboratory, Zhengzhou University, Zhengzhou, 450001, China. Electronic address: liucs@gs.zzu.edu.cn.
² Yellow River Laboratory, Zhengzhou University, Zhengzhou, 450001, China.
³ Yellow River Laboratory, Zhengzhou University, Zhengzhou, 450001, China. Electronic address: wuqianghlj@163.com.
⁴ Yellow River Laboratory, Zhengzhou University, Zhengzhou, 450001, China. Electronic address: hucaihong@zzu.edu.cn.

PMID: 37413729
DOI: 10.1016/j.jenvman.2023.118482

Abstract

In recent years, urban flood disasters caused by sudden heavy rains have become increasingly severe, posing a serious threat to urban public infrastructure and the life and property safety of residents. Rapid simulation and prediction of urban rain-flood events can provide timely decision-making reference for urban flood control and disaster reduction. The complex and arduous calibration process of urban rain-flood models has been identified as a major obstacle affecting the efficiency and accuracy of simulation and prediction. This study proposes a multi-scale urban rain-flood model rapid construction method framework, BK-SWMM, focusing on urban rain-flood model parameters and based on the basic architecture of Storm Water Management Model (SWMM). The framework comprises two main components: 1) constructing a SWMM uncertainty parameter sample crowdsourcing dataset and coupling Bayesian Information Criterion (BIC) and K-means clustering machine learning algorithm to discover clustering patterns of SWMM model uncertainty parameters in urban functional areas; 2) coupling BIC and K-means with SWMM model to form BK-SWMM flood simulation framework. The applicability of the proposed framework is validated by modeling three different spatial scales in the study regions based on observed rainfall-runoff data. The research findings indicate that the distribution pattern of uncertainty parameters, such as depression storage, surface Manning coefficient, infiltration rate, and attenuation coefficient. The distribution patterns of these seven parameters in urban functional zones indicate that the values are highest in the Industrial and Commercial Areas (ICA), followed by Residential Areas (RA), and lowest in Public Areas (PA). All three spatial scales' RE_Q, NSE_Q, and R_D² indices were superior to the SWMM and less than 10%, greater than 0.80, and greater than 0.85, respectively. However, when the study area's geographical scale expands, the simulation's accuracy will decline. Further research is required on the scale dependency of urban storm flood models.

Keywords: BK-SWMM framework; Storm flood simulation; Sub-catchment area division; Uncertainty parameters; Urban functional area.

MeSH terms

Bayes Theorem
China
Cities
Crowdsourcing*
Floods*
Models, Theoretical
Rain
Uncertainty
Water
Water Movements

Substances

Water