Precipitation, tidal and river level impacts on influent volumes of combined wastewater collection systems: A regional analysis

Saikia, Sukanya D.; Ryan, Paraic C.; Nuyts, Siegmund; Clifford, Eoghan

doi:10.1016/j.rineng.2022.100588

Cited by 6 publications

(2 citation statements)

References 26 publications

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“…Especially during heavy rainfall periods, continuous rainfall increases the pressure in the rainwater–sewage combined sewage pipeline, intensifies sewage infiltration into the surrounding soil layers, increases soil moisture, and raises the groundwater level . After the rainfall stops, the pressure in the pipeline decreases, and the sewage stored in the soil flows back into the pipeline due to the pressure difference . Therefore, the impact of precipitation on the influent flow rate can last for several days after the rainfall stops, depending on factors such as pipeline materials and damage, geological properties, and groundwater level.…”

Section: Resultsmentioning

confidence: 99%

“…42 After the rainfall stops, the pressure in the pipeline decreases, and the sewage stored in the soil flows back into the pipeline due to the pressure difference. 44 Therefore, the impact of precipitation on the influent flow rate can last for several days after the rainfall stops, depending on factors such as pipeline materials and damage, geological properties, and groundwater level. According to the comparative performance of the model in Section 3.1, the impact of precipitation on the influent flow rate of the three wastewater treatment plants in the study area is approximately 5−10 days.…”

Section: Role Of Different Predictorsmentioning

confidence: 99%

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

Wei,

Yu,

Tian

et al. 2023

ACS EST Water

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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 (NH 3 −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.

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Section: Resultsmentioning

confidence: 99%

Section: Role Of Different Predictorsmentioning

confidence: 99%