Prediction of River Pollution Under the Rainfall-Runoff Impact by Artificial Neural Network: A Case Study of Shiyan River, Shenzhen, China

Tian, Zhan1,2; Yu, Ziwei1; Li, Yifan1; Ke, Qian3; Liu, Junguo1; Luo, Hongyan4; Tang, Yingdong5

2022-06-22

10.3389/fenvs.2022.887446

FRONTIERS IN ENVIRONMENTAL SCIENCE   影响因子和分区

2296-665X

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.

rainfall-runoff pollution rainfall characteristics EMC integrated learning methods ANN

SCI

英语

第一 ; 通讯

Environmental Sciences & Ecology

Environmental Sciences

WOS:000821276200001

FRONTIERS MEDIA SA

Web of Science

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

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.

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.

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).