Real-time sensor-based prediction of soil moisture in green infrastructure: A case study

Green infrastructure (GI) is cost-effective for managing urban runoff. However, inspection and maintenance of GI are an increasingly common burden for stormwater managers. For instance, bioretention cells, a popular type of GI, may clog, sometimes unexpectedly, and detection can be challenging due to their dispersed placement. Current inspection programs nationwide largely rely on time-intensive, manual, qualitative inspections. This study develops an approach for real-time monitoring and prediction of column performance. First, we conduct laboratory experiments to continuously collect soil moisture data using sensors at two different depths in bioretention column testbeds. Four design configurations are used that allow the water to drain differently through the column, hence acting as different environmental climates. Next, we develop machine learning models, i.e., long short-term memory (LSTM) models, to accurately predict current and future soil moisture levels. Our results suggest that the quality of predictions is overall high, but they vary across the configurations. •Green infrastructure (GI) can mitigate urban runoff.•GI is prone to clogging, but detecting/addressing issues can be challenging.•Real-time sensor-based monitoring can track GI soil moisture levels.•Machine learning models can use sensor data to predict GI performance.