Article ; Online: An integrated modelling framework for multiple pollution source identification in surface water.
Journal of environmental management
2023 Volume 347, Page(s) 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 ... ...
| 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. |
|---|---|
| MeSH term(s) | Neural Networks, Computer ; Algorithms ; Computer Simulation ; Water Quality ; Rivers |
| Language | English |
| Publishing date | 2023-09-29 |
| Publishing country | England |
| Document type | Journal Article |
| ZDB-ID | 184882-3 |
| ISSN | 1095-8630 ; 0301-4797 |
| ISSN (online) | 1095-8630 |
| ISSN | 0301-4797 |
| DOI | 10.1016/j.jenvman.2023.119126 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
Full text online
More links
Kategorien
In stock of ZB MED Bonn / Germany
| Z 7556: Show issues |
Order via subito
This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.
Inter-library loan at ZB MED
Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.