An integrated modelling framework for multiple pollution source identification in surface water

Xiaodong Liu; Xuneng Tong; Lei Wu; Sanjeeb Mohapatra; Hongqin Xue; Ruochen Liu

doi:10.1016/j.jenvman.2023.119126

An integrated modelling framework for multiple pollution source identification in surface water

J Environ Manage. 2023 Dec 1:347:119126. doi: 10.1016/j.jenvman.2023.119126. Epub 2023 Sep 29.

Authors

Xiaodong Liu¹, Xuneng Tong², Lei Wu³, Sanjeeb Mohapatra⁴, Hongqin Xue⁵, Ruochen Liu⁶

Affiliations

¹ Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes of Ministry of Education, College of Environment, Hohai University, Nanjing 210098, China; Yangtze Institute for Conservation and Development, Hohai University, Jiangsu 210098, China.
² Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes of Ministry of Education, College of Environment, Hohai University, Nanjing 210098, China; Department of Civil and Environmental Engineering, National University of Singapore, 1 Engineering Drive 2, Singapore, 117576, Singapore. Electronic address: xunengtong@u.nus.edu.
³ Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes of Ministry of Education, College of Environment, Hohai University, Nanjing 210098, China.
⁴ E2S2-CREATE, NUS Environmental Research Institute, National University of Singapore, 1 Create Way, Create Tower, #15-02, Singapore 138602, Singapore.
⁵ School of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China.
⁶ Jiangsu Suli Environmental Technology Co., Ltd., Nanjing, Jiangsu 210036, China.

PMID: 37778063
DOI: 10.1016/j.jenvman.2023.119126

Abstract

Pollution source identification is vital in water safety management. An integrated simulation-optimization modelling framework comprising a process-based hydrodynamic water quality model, artificial neural network surrogate model and particle swarm optimization (PSO) was proposed to achieve rapid, accurate and reliable pollution source identification. In this study, the hydrodynamics and water quality processes in a straight lab-based flume were simulated to test pollution source identification under steady flow conditions. Additionally, the pollution source identification in the unsteady flow conditions was examined using a real-life estuary, specifically the Yangtze River estuary. First, we developed two process-based models to simulate hydrodynamics and water quality in the flume and estuary. Then, the data generated from the process-based models were used to develop surrogate models. Three typical artificial neural networks (ANNs) algorithms: backpropagation (BP), radial basis function (RBF) and general regression neural networks (GRNN) were selected to develop surrogates for process-based models (PBMs), and they were coupled with PSO algorithm to achieve the hybrid modelling framework for pollution source identification. Our results showed that hybrid PBM-ANNs-PSO models could be applied to identify the pollution source and quantify release intensity in spatial distribution when the discharge type was assumed as the point source with a continuous release. Multiple-performance criteria metrics, in terms of the coefficient of determination, root-mean-square error, mean absolute error, evaluated the model performance as "Excellent prediction". The BP-PSO models consistently appear to be the top-performing source identification model within the developed models, with most cases of relative error (RE) values lower than 5%. The new insights from the hybrid modelling framework would provide useful information for the local government agency to make reasonable decisions regarding pollution source identification issues.

Keywords: Backpropagation; General regression; Particle swarm optimization; Pollution source identification; Radial basis function; Surrogate modelling.

MeSH terms

Algorithms*
Computer Simulation
Neural Networks, Computer*
Rivers
Water Quality