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Genetic algorithm for contaminant source characterization using imperfect sensors

Abstract A simple, straightforward, modified genetic algorithm scheme for contaminant source characterization using imperfect sensors is presented and demonstrated in this study. Previous work on this subject concentrated on developing source-inversion models using sensors that provide accurate, unbiased, contamination concentration measurements. The developed contamination source-detection model is implemented using three sensor types: (1) perfect sensors providing accurate, unbiased, contamination concentration measurements; (2) sensors transmitting fuzzy measured information (i.e., high, medium, and low contamination); and (3) ‘0–1’ (Boolean) sensors indicating only a contamination presence. A comparison between the three sensor types is explored taking into consideration thesystems response time (i.e., the time elapsed between a contaminant detection and a decision-makers response action). The methodology capabilities are demonstrated using two example applications of increasing complexity, showing the trade-offs between the sensor types and the model abilities to receive a unique solution to the source-detection problem.

WaterWiSe@SG : a testbed for continuous monitoring of the water distribution system in Singapore

This paper describes the development of WaterWiSe@SG, a wireless sensor network to enable real-time monitoring of a water distribution network in Singapore. The overall project is directed towards three main goals: 1) the application of a low cost wireless sensor network for high data rate, on-line monitoring of hydraulic parameters within a large urban water distribution system; 2) the development of systems to enable remote detection of leaks and prediction of pipe burst events; 3) the integrated monitoring of hydraulic and water quality parameters. In this paper we will describe the current state of the WaterWiSe@SG testbed, and report on experimentation we have performed with respect to leak detection and localization. Furthermore, we describe how we have assimilated real time pressure and flow measurements from the sensor network into hydraulic models that are used to improve state estimation for the network. Finally, we discuss the future plans for the project.

Wavelet-based Burst Event Detection and Localization in Water Distribution Systems

In this paper we present techniques for detecting and locating transient pipe burst events in water distribution systems. The proposed method uses multiscale wavelet analysis of high rate pressure data recorded to detect transient events. Both wavelet coefficients and Lipschitz exponents provide additional information about the nature of the signal feature detected and can be used for feature classification. A local search method is proposed to estimate accurately the arrival time of the pressure transient associated with a pipe burst event. We also propose a graph-based localization algorithm which uses the arrival times of the pressure transient at different measurement points within the water distribution system to determine the actual location (or source) of the pipe burst. The detection and localization performance of these algorithms is validated through leak-off experiments performed on the WaterWiSe@SG wireless sensor network test bed, deployed on the drinking water distribution system in Singapore. Based on these experiments, the average localization error is 37.5 m. We also present a systematic analysis of the sources of localization error and show that even with significant errors in wave speed estimation and time synchronization the localization error is around 56 m.

Efficient Hydraulic State Estimation Technique Using Reduced Models of Urban Water Networks

This paper describes and demonstrates an efficient method for online hydraulic state estimation in urban water networks. The proposed method employs an online predictor-corrector (PC) procedure for forecasting future water demands. A statistical data-driven algo- rithm (M5 Model-Trees algorithm) is applied to estimate future water demands, and an evolutionary optimization technique (genetic algo- rithms) is used to correct these predictions with online monitoring data. The calibration problem is solved using a modified least-squares (LS) fit method (Huber function) in which the objective function is the minimization of the residuals between predicted and measured pressure at several system locations, with the decision variables being the hourly variations in water demands. To meet the computational efficiency requirements of real-time hydraulic state estimation for prototype urban networks that typically comprise tens of thousands of links and nodes, a reduced model is introduced using a water system-aggregation technique. The reduced model achieves a high-fidelity representation for the hydraulic performance of the complete network, but greatly simplifies the computation of the PC loop and facilitates the implemen- tation of the online model. The proposed methodology is demonstrated on a prototypical municipal water-distribution system. DOI: 10.1061/ (ASCE)WR.1943-5452.0000113.

MULTIOBJECTIVE SENSOR DESIGN FOR WATER DISTRIBUTION SYSTEMS SECURITY

This paper presents a multiobjective model to optimal sensor design in water distribution systems as part of the battle of the water sensors networks (BWSN). Previous work on optimal sensor design for water distribution systems (WDS) focused on one objective (e.g., maximizing the detection likelihood of contamination events). In this study the Non-Dominated Sorted Genetic Algorithm–II (NSGA-II) is implemented to tradeoff the following four conflicting objectives: (1) maximizing the detection likelihood; (2) minimizing the detection time; (3) maximizing the detection instrumentation redundancy; and (4) maximizing the contamination source identification likelihood (i.e., the likelihood to provide a unique solution to the inverse problem of contamination source identification for a given layout of sensors). The effectiveness of the multiobjective approach is demonstrated through using the two BWSN network examples, where Pareto fronts are plotted for each two objectives; for each three; and finally for all four.

A contamination source identification model for water distribution system security

This article presents and demonstrates a simple, straightforward genetic algorithm (GA) scheme for contamination source identification to enhance the security of water distribution systems. Related previous work on this subject has concentrated on developing analytical water quality inverse models with two major restrictions: the ability to disclose unique solutions and to handle water distribution systems of large size. These two limitations are addressed in this study by coupling a GA with EPANET. The objective function is minimization of the least-squares of the differences between simulated and measured contaminant concentrations, with the decision variables being the contaminant event characteristics of intrusion location, starting time, duration and mass rate. The developed methodology is demonstrated through base runs and sensitivity analysis of three water distribution system example applications of increasing complexity.

Water Main Burst Event Detection and Localization

In this paper we present a technique for detecting and locating burst events in pipelines. The proposed method uses wavelet analysis of the high-rate pressure data to detect pipe burst events. Multiscale wavelet analysis of the pressure signal will be shown to be robust to impulsive noise encountered in the physical phenomena under observation. The wavelet coefficients also allow us to obtain additional information about the nature of the signal feature detected, which can used for further feature classification. A local search method is also proposed to accurately determine the arrival time of the pressure front associated with the burst event. The detection performance of these algorithms is verified through leak-off experiments performed on the WaterWiSe@SG test bed deployed on the water distribution system in Singapore. We also propose a graph-based search algorithm which uses the arrival times of the pressure front at different locations within the water distribution system to determine the actual location of the pipe burst event.

Online Hydraulic State Prediction for Water Distribution Systems

World Environmental and Water Resources Congress 2009: Great Rivers Proceedings of World Environmental and Water Resources Congress 2009 May 17–21, 2009 Kansas City, Missouri

Multi-objective Sensor Placements with Improved Water Quality Models in a Network with Multiple Junctions

Concerns about the security of water distribution systems have lead to increased interest in sensor placement in water distribution systems. Due to the cost of both placing and maintaining these sensors, the number of sensors used must be limited. These constraints make the sensor deployment locations crucial in a water monitoring system. Many studies, based on differing algorithms and objective functions, have sought to determine ways to optimize sensor location. These studies have largely relied on current water quality models that assume perfect mixing at pipe junctions. However, it has been shown that using a water quality model that accounts for imperfect mixing (AZRED) at pipe intersections produces outcomes that differ from those produced by studies that assume perfect mixing and, consequently produces a different scheme for optimal sensor placement. The current work uses a multiobjective approach that relies on the nondominated, sorted algorithm II. The study seeks, first, to contrast the use of the AZRED water-quality model to the use of water quality models that assume perfect mixing, and, second, to propose a more comprehensive approach to sensor placement. By using a simpler objective of optimizing for complete sensor coverage, the study will expand on pervious work that made this comparison. An example network is analyzed using both AZRED and EPANET, and the results are compared.

Hydraulic uncertainty inclusion in water distribution systems contamination source identification

This study presents a methodology for the inclusion of hydraulics uncertainty in contamination source identification. Current research normally considers the system hydraulics as deterministic and the water quality sensors as ideal. In reality however only a small portion of the hydraulic data is known and most likely only Boolean sensor information of a contamination existence. There is a need to incorporate these considerations in contamination source identification models and to explore their influence on the modelling ability to correctly detect the characteristics of a contamination intrusion. This problem is addressed in this manuscript. The proposed method is based on a previous contamination source detection model developed by the authors which is further embedded in a statistical framework for quantifying the uncertainty of a contamination source detection outcome. The methodology is demonstrated on three example applications of increasing complexity through base runs and sensitivity analyses.