Stormwater management seeks to reduce runoff from rain or melted snow and improve water quality. Where it can absorb into soil, runoff is filtered and returns to streams, rivers, and aquifers, but in developed areas, precipitation often cannot soak into the ground because impervious surfaces (e.g., pavement, buildings), and already saturated soils can create excess runoff. This water, which can contain pollutants, then runs across urban surfaces and into storm drains, drainage ditches, and sewer systems. Stormwater runoff can cause flooding, erosion, infrastructure and habitat damage, and contamination (including combined and sanitary sewer overflows). In urban and developed areas, effective stormwater management that routes and detains stormwater helps to mitigate these impacts and improve water quality.

Smart stormwater systems will transform cities into coordinated and real-time controlled treatment plants.

“Smart” water systems are transforming the field of stormwater management by enabling real-time monitoring and control of previously static infrastructure. While the localized benefits of active control are well-established, the potential for system-scale control of watersheds is poorly understood. This study shows how a real-world smart stormwater system can be leveraged to shape streamflow within an urban watershed. Specifically, we coordinate releases from two internet-controlled stormwater basins to achieve desired control objectives downstream—such as maintaining the flow at a set-point, and generating interleaved waves. In the first part of the study, we describe the construction of the control network using a low-cost, open-source hardware stack and a cloud-based controller scheduling application. Next, we characterize the system’s control capabilities by determining the travel times, decay times, and magnitudes of various waves released from the upstream retention basins. With this characterization in hand, we use the system to generate two desired responses at a critical downstream junction. First, we generate a set-point hydrograph, in which flow is maintained at an approximately constant rate. Next, we generate a series of overlapping and interleaved waves using timed releases from both retention basins. We discuss how these control strategies can be used to stabilize flows, thereby mitigating streambed erosion and reducing contaminant loads into downstream waterbodies.

In this paper, we present the first open-source implementation of the robust random cut forest (RRCF) algorithm-an unsupervised ensemble method for anomaly detection on streaming data (Guha, Mishra, Roy, & Schrijvers, 2016). RRCF offers a number of features that many competing anomaly detection algorithms lack. Specifically, RRCF: • Is designed to handle large volumes of streaming data. • Is well-suited to data of high dimension. • Reduces the influence of irrelevant dimensions in the input data. • Gracefully handles duplicates and near-duplicates that could otherwise mask the presence of outliers. • Features an anomaly-scoring metric with a clear underlying statistical meaning. The RRCF algorithm is currently used for anomaly detection in the Amazon Kinesis real-time analytics engine. The goal of our repository is to provide an open-source implementation of the RRCF algorithm and its core data structures for the purposes of facilitating experimentation and enabling future extensions.