TY - JOUR
T1 - Prediction of River Pollution Under the Rainfall-Runoff Impact by Artificial Neural Network
T2 - A Case Study of Shiyan River, Shenzhen, China
AU - Tian, Zhan
AU - Yu, Ziwei
AU - Li, Yifan
AU - Ke, Qian
AU - Liu, Junguo
AU - Luo, Hongyan
AU - Tang, Yingdong
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - ANN
KW - EMC
KW - integrated learning methods
KW - rainfall characteristics
KW - rainfall-runoff pollution
UR - http://www.scopus.com/inward/record.url?scp=85133716528&partnerID=8YFLogxK
U2 - 10.3389/fenvs.2022.887446
DO - 10.3389/fenvs.2022.887446
M3 - Article
AN - SCOPUS:85133716528
SN - 2296-665X
VL - 10
JO - Frontiers in Environmental Science
JF - Frontiers in Environmental Science
M1 - 887446
ER -