Comparative Performance of Three Machine Learning Models in Predicting Influent Flow Rates and Nutrient Loads at Wastewater Treatment Plants

Wei, Xiaoou1,2,3; Yu, Jiang4; Tian, Yong2,3; Ben, Yujie2,3; Cai, Zongwei1; Zheng, Chunmiao2,3,4

2023-09-01

10.1021/acsestwater.3c00155

ACS ES&T WATER   Impact Factor & Quartile

2690-0637

Accurately predicting influent wastewater quality is vital for the efficient operation and maintenance of wastewater treatment plants (WWTPs). This study evaluated three machine learning (ML) models for predicting influent flow rates and nutrient loads of both industrial and domestic wastewaters in WWTPs. These predictions were based on meteorological data and the population migration patterns. The models?random forest, extra trees, and gradient boosting regressor?were successfully applied to three full-scale WWTPs in Shenzhen, China. All the models demonstrated robust performance in predicting influent flow rate, ammoniacal nitrogen (NH3-N), and total nitrogen (TN). Feature importance analysis revealed that the average precipitation over the past n days and population migration were the most influential factors for predicting influent flow rate. Conversely, human activities have a greater impact on pollutant concentrations. Scenario analyses indicated that precipitation contributed to approximately 5%-10% of the wastewater influent, while groundwater infiltration accounted for around 20%. Overall, this study provides a model framework for forecasting wastewater loads using meteorological and population migration data, setting the groundwork for smart management in WWTPs.

wastewater treatment plants machine learning prediction model wastewater influent loads

ESCI

English

Corresponding

Environmental Sciences & Ecology ; Water Resources

Environmental Sciences ; Water Resources

WOS:001071976700001

AMER CHEMICAL SOC

Web of Science

1.Hong Kong Baptist Univ, Dept Chem, State Key Lab Environm & Biol Anal, Hong Kong 999077, Peoples R China
2.Southern Univ Sci & Technol, State Environm Protect Key Lab Integrated Surface, Sch Environm Sci & Engn, Shenzhen 518055, Peoples R China
3.Southern Univ Sci & Technol, Sch Environm Sci & Engn, Guangdong Prov Key Lab Soil & Groundwater Pollut, Shenzhen 518055, Peoples R China
4.Eastern Inst Technol, Eastern Inst Adv Study, Ningbo 315200, Peoples R China

Wei, Xiaoou,Yu, Jiang,Tian, Yong,et al. Comparative Performance of Three Machine Learning Models in Predicting Influent Flow Rates and Nutrient Loads at Wastewater Treatment Plants[J]. ACS ES&T WATER,2023.

Wei, Xiaoou,Yu, Jiang,Tian, Yong,Ben, Yujie,Cai, Zongwei,&Zheng, Chunmiao.(2023).Comparative Performance of Three Machine Learning Models in Predicting Influent Flow Rates and Nutrient Loads at Wastewater Treatment Plants.ACS ES&T WATER.

Wei, Xiaoou,et al."Comparative Performance of Three Machine Learning Models in Predicting Influent Flow Rates and Nutrient Loads at Wastewater Treatment Plants".ACS ES&T WATER (2023).