Turbidity informed real-time control of a dry extended detention basin: a case study

• Published in: Environmental science water research & technology 2022-10, Vol.8 (1), p.24-251
• Main Authors: Akin, Aaron A, Hathaway, Jon M, Khojandi, Anahita
• Format: Article
• Language: English
• Subjects: Adaptive systems; Basins; Controllability; Detention basins; Detention time; Economic models; Hydrologic data; Long short-term memory; Long-term changes; Precipitation; Rainfall; Real time; Regression analysis; Regression models; Resource allocation; Retrofitting; Sensors; Stormwater; Turbidimeters; Turbidity; Urbanization; Water quality; Water quality control; Watersheds
• ISSN: 2053-1400; 2053-1419
• DOI: 10.1039/d1ew00654a

Dry extended detention basins are static stormwater infrastructure, unable to adapt to shifts in water quality caused by urbanization in their source watersheds or long-term changes in rainfall patterns. As a potential solution to these problems, this research investigated the impact and use of real-time water quality data in a dry extended detention basin retrofitted with a controllable valve and a turbidity sensor with the goal of more consistently meeting water quality objectives. When rainfall was detected, the basin's valve would close and detain all water until either a maximum allowable detention time was reached, or turbidity values fell below a predetermined threshold. This method was shown to produce highly variable detention times after rainfall events which highlights the advantages an adaptive system has over a traditional static system or one which uses predetermined detention times to meet water quality objectives. To investigate if turbidity-based controls could operate effectively in the future if the turbidity sensor were to be removed, an advantage for economical resource allocation, several modeling approaches were evaluated to estimate the detention time of the system based on observed basin stage and precipitation data. Two of these models, a logistic regression model and a long short-term memory (LSTM) model, proved accurate in estimating the necessary detention time of the system. With this system's ability to meet water quality objectives more consistently when real-time water quality data were integrated into the decision framework, this study lays the groundwork for other applications where improved water quality is the goal. This research investigated the impact and use of real-time water quality data in a dry extended detention basin retrofitted with a controllable valve and a turbidity sensor with the goal of more consistently meeting water quality objectives.