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

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.

Implications of Dynamic Interactions between Meteorological Patterns and Surface Water Quality on Environmental Health—A Case Study of the Nairobi River

“…The multifaceted impacts of rainfall on water quality have therefore been noted multiple times, emphasizing the importance of proactive measures to mitigate adverse effects. Understanding the complex interactions between rainfall dynamics and water quality parameters is essential for effective water resource management and the development of resilient infrastructure and policies to safeguard public health and environmental integrity (Tian et al, 2022).…”