| Title | Prediction of River Pollution Under the Rainfall-Runoff Impact by Artificial Neural Network: A Case Study of Shiyan River, Shenzhen, China |
| Author | |
| Corresponding Author | Liu, Junguo |
| Publication Years | 2022-06-22
|
| DOI | |
| Source Title | |
| EISSN | 2296-665X
|
| Volume | 10 |
| Abstract | Climate change and rapid urbanization have made it difficult to predict the risk of pollution in cities under different types of rainfall. In this study, a data-driven approach to quantify the effects of rainfall characteristics on river pollution was proposed and applied in a case study of Shiyan River, Shenzhen, China. The results indicate that the most important factor affecting river pollution is the dry period followed by average rainfall intensity, maximum rainfall in 10 min, total amount of rainfall, and initial runoff intensity. In addition, an artificial neural network model was developed to predict the event mean concentration (EMC) of COD in the river based on the correlations between rainfall characteristics and EMC. Compared to under light rain (< 10 mm/day), the predicted EMC was five times lower under heavy rain (25-49.9 mm/day) and two times lower under moderate rain (10-24.9 mm/day). By converting the EMC to chemical oxygen demand in the river, the pollution load under non-point-source runoff was estimated to be 497.6 t/year (with an accuracy of 95.98%) in Shiyan River under typical rainfall characteristics. The results of this study can be used to guide urban rainwater utilization and engineering design in Shenzhen. The findings also provide insights for predicting the risk of rainfall-runoff pollution and developing related policies in other cities. |
| Keywords | |
| URL | [Source Record] |
| Indexed By | |
| Language | English
|
| SUSTech Authorship | First
; Corresponding
|
| WOS Research Area | Environmental Sciences & Ecology
|
| WOS Subject | Environmental Sciences
|
| WOS Accession No | WOS:000821276200001
|
| Publisher | |
| Data Source | Web of Science
|
| Citation statistics |
Cited Times [WOS]:0
|
| Document Type | Journal Article |
| Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/355870 |
| Department | School of Environmental Science and Engineering |
| Affiliation | 1.Southern Univ Sci & Technol, Sch Environm Sci & Engn, Shenzhen, Peoples R China 2.Pengcheng Lab, Shenzhen, Peoples R China 3.Delft Univ Technol, Delft, Netherlands 4.Meteorol Bur Shenzhen Municipal, Shenzhen, Peoples R China 5.PowerChina Huadong Engn Corp Ltd, Hangzhou, Peoples R China |
| First Author Affilication | School of Environmental Science and Engineering |
| Corresponding Author Affilication | School of Environmental Science and Engineering |
| First Author's First Affilication | School of Environmental Science and Engineering |
| Recommended Citation GB/T 7714 |
Tian, Zhan,Yu, Ziwei,Li, Yifan,et al. Prediction of River Pollution Under the Rainfall-Runoff Impact by Artificial Neural Network: A Case Study of Shiyan River, Shenzhen, China[J]. FRONTIERS IN ENVIRONMENTAL SCIENCE,2022,10.
|
| APA |
Tian, Zhan.,Yu, Ziwei.,Li, Yifan.,Ke, Qian.,Liu, Junguo.,...&Tang, Yingdong.(2022).Prediction of River Pollution Under the Rainfall-Runoff Impact by Artificial Neural Network: A Case Study of Shiyan River, Shenzhen, China.FRONTIERS IN ENVIRONMENTAL SCIENCE,10.
|
| MLA |
Tian, Zhan,et al."Prediction of River Pollution Under the Rainfall-Runoff Impact by Artificial Neural Network: A Case Study of Shiyan River, Shenzhen, China".FRONTIERS IN ENVIRONMENTAL SCIENCE 10(2022).
|
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
;
Edit Comment