Water Quality Event Detection Research Articles

Development of a Water Quality Event Detection and Diagnosis Framework in Drinking Water Distribution Systems with Structured and Unstructured Data Integration

Recently, various detection approaches that identify anomalous events (e.g., discoloration, contamination) by analyzing data collected from smart meters (so-called structured data) have been developed for many water distribution systems (WDSs). However, although some of them have showed promising results, meters often fail to collect/transmit the data (i.e., missing data) thus meaning that these methods may frequently not work for anomaly identification. Thus, the clear next step is to combine structured data with another type of data, unstructured data, that has no structural format (e.g., textual content, images, and colors) and can often be expressed through various social media platforms. However, no previous work has been carried out in this regard. This study proposes a framework that combines structured and unstructured data to identify WDS water quality events by collecting turbidity data (structured data) and text data uploaded to social networking services (SNSs) (unstructured data). In the proposed framework, water quality events are identified by applying data-driven detection tools for the structured data and cosine similarity for the unstructured data. The results indicate that structured data-driven tools successfully detect accidents with large magnitudes but fail to detect small failures. When the proposed framework is used, those undetected accidents are successfully identified. Thus, combining structured and unstructured data is necessary to maximize WDS water quality event detection.

Modelling of resuspension due to fish activity: Mathematical modeling and annular flume experiments

A spatially averaged numerical model was developed to describe the erosion of cohesive sediment. Together with known empirical relations, the model comprises a new formulation for resuspension due to fish activity. Experiments on erosion of natural sediments in the annular flume at Aachen University are used for model calibration. Empirical coefficients were evaluated with genetic algorithms to achieve the best agreement between the model results and the experimental data. The presented model shows sufficient flexibility to account for various sediment properties, including different sediment sources, natural and artificial contaminants, presence or absence of aquatic organisms, and results in an average coefficient of determination, R2 = 90.5% between the model results and the experimental data. Model validation allows it to be assumed that different contaminants affect bed properties differently. Fish activity plays an essential role in correct resuspension prediction. Further sediment erosion experiments with carefully chosen conditions will allow a more comprehensive model evaluation. The presented model is intended to serve as a building block in the development of a hydraulic-sediment-biota model within the W3-Hydro: Water Quality Event Detection for Urban Water Security and Urban Water Management Based on Hydrotoxicological Investigations project that aims to improve the knowledge concerning bioavailability, transport, fate, and effects of contaminants on the aquatic environment.

CANARY Eases Water Quality Event Detection.

A free software tool from the US Environmental Protection Agency was put through its paces at a midsize water utility and bolstered the utility's water quality event-detection capabilities.

Network hydraulics inclusion in water quality event detection using multiple sensor stations data

Event detection is one of the current most challenging topics in water distribution systems analysis: how regular on-line hydraulic (e.g., pressure, flow) and water quality (e.g., pH, residual chlorine, turbidity) measurements at different network locations can be efficiently utilized to detect water quality contamination events. This study describes an integrated event detection model which combines multiple sensor stations data with network hydraulics. To date event detection modelling is likely limited to single sensor station location and dataset. Single sensor station models are detached from network hydraulics insights and as a result might be significantly exposed to false positive alarms. This work is aimed at decreasing this limitation through integrating local and spatial hydraulic data understanding into an event detection model. The spatial analysis complements the local event detection effort through discovering events with lower signatures by exploring the sensors mutual hydraulic influences. The unique contribution of this study is in incorporating hydraulic simulation information into the overall event detection process of spatially distributed sensors. The methodology is demonstrated on two example applications using base runs and sensitivity analyses. Results show a clear advantage of the suggested model over single-sensor event detection schemes.

Water Quality Event Detection in Drinking Water Network

Drinking water network is vulnerable to toxic chemicals. Anomaly detection-based event detection can provide reliable indication of contamination by analyzing the real-time water quality data, collected by online-distributed sensors in water network. This article reviews the water quality event detection methodologies based on the correlation of water quality parameters and contaminants. Further, we review how to reduce the impact of contamination in water distribution network, including sensor placement optimization and contamination source determination.

Methods for Water Quality Abnormity Assessment Based on Single Factor

The classification methods for water quality event detection are discussed, and the feasibility of water quality event detection based on single factor analysis is confirmed through the experiment. The process of water quality detection is introduced and one of Linear Filter algorithms is investigated. The test shows that autoregressive algorithm performs well for water quality abnormity detection.

Water quality event detection and customer complaint clustering analysis in distribution systems

Safe, clean drinking water is a foundation of society and water quality monitoring can contribute to ensuring this. A case study application of the CANARY software to historic data from a UK drinking water distribution system is described. Sensitivity studies explored appropriate choice of algorithmic parameter settings for a baseline site, performance was evaluated with artificial events and the system then transferred to all sites. Results are presented for analysis of nine water quality sensors measuring six parameters and deployed in three connected district meter areas (DMAs), fed from a single water source (service reservoir), for a 1 year period and evaluated using comprehensive water utility records with 86% of event clusters successfully correlated to causes (spatially limited to DMA level). False negatives, defined by temporal clusters of water quality complaints in the pilot area not corresponding to detections, were only approximately 25%. It was demonstrated that the software could be configured and applied retrospectively (with potential for future near real time application) to detect various water quality event types (with a wider remit than contamination alone) for further interpretation.

Real-Time Water Quality Monitoring: Assessment of Multisensor Data Using Bayesian Belief Networks

Real-time sensing in water distribution systems provides a potentially powerful analytical tool for providing water security. Through monitoring surrogate parameters (e.g., pH, turbidity, and residual chlorine) over time, the natural variations of a distribution system’s parameters are established, allowing rapid detection of changes in water quality. However, the level of performance that water quality event detection algorithms have exhibited to date is insufficient for real-world utilization. Bayesian belief networks (BBNs) offer a formalized method of reasoning under uncertainty and are well suited to the analysis of multiple sources of information. The application of a BBN to water quality event detection is described. Surrogate parameters (pH, conductivity, and turbidity) were monitored during an experimental E. coli contamination. Difference filtration using a 60-s moving window of observations identified rapid rates of change present in the surrogate parameter signals, demonstrated as responsive to contamination as simulated in bench-scale studies. A BBN was constructed to assimilate the surrogate parameter variation and compute temporal probability distributions about the contamination of an experimental system. The BBN topology, probability distributions and data transformation techniques applied were validated through successful identification of contaminant injections.

Distributed Sensor Fusion in Water Quality Event Detection

To protect drinking water systems, a contamination warning system can use in-line sensors to indicate possible accidental and deliberate contamination. Currently, reporting of an incident occurs when data from a single station detects an anomaly. This paper proposes an approach for combining data from multiple stations to reduce false background alarms. By considering the location and time of individual detections as points resulting from a random space-time point process, Kulldorff’s scan test can find statistically significant clusters of detections. Using EPANET to simulate contaminant plumes of varying sizes moving through a water network with varying amounts of sensing nodes, it is shown that the scan test can detect significant clusters of events. Also, these significant clusters can reduce the false alarms resulting from background noise and the clusters can help indicate the time and source location of the contaminant. Fusion of monitoring station results within a moderately sized network show fal...