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CANARY: A water quality event detection algorithm development tool

Restoring Our Natural Habitat - Proceedings of the 2007 World Environmental and Water Resources Congress

Hart, David; Mckenna, Sean A.; Klise, Katherine A.; Cruz, Victoria; Wilson, Mark

The detection of anomalous water quality events has become an increased priority for distribution systems, both for quality of service and security reasons. Because of the high cost associated with false detections, both missed events and false alarms, algorithms which aim to provide event detection aid need to be evaluated and configured properly. CANARY has been developed to provide both real-time, and off-line analysis tools to aid in the development of these algorithms, allowing algorithm developers to focus on the algorithms themselves, rather than on how to read in data and drive the algorithms. Among the features to be discussed and demonstrated are: 1) use of a standard data exchange format for input and output of water quality and operations data streams; 2) the ability to "plug in" various water quality change detection algorithms, both in MATLAB® and compiled library formats for testing and evaluation by using a well defined interface; 3) an "operations mode" to simulate what a utility operator will receive; 4) side-by-side comparison tools for different evaluation metrics, including ROC curves, time to detect, and false alarm rates. Results will be shown using three algorithms previously developed (Klise and McKenna, 2006; McKenna, et al., 2006) using test and real-life data sets. © 2007 ASCE.

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TYPE Conference YEAR 2007
ScopusOSTI

Predicting building contamination using machine learning

Proceedings - 6th International Conference on Machine Learning and Applications, ICMLA 2007

Martin, Shawn; Mckenna, Sean A.

Potential events involving biological or chemical contamination of buildings are of major concern in the area of homeland security. Tools are needed to provide rapid, onsite predictions of contaminant levels given only approximate measurements in limited locations throughout a building. In principal, such tools could use calculations based on physical process models to provide accurate predictions. In practice, however, physical process models are too complex and computationally costly to be used in a real-time scenario. In this paper, we investigate the feasibility of using machine learning to provide easily computed but approximate models that would be applicable in the field. We develop a machine learning method based on Support Vector Machine regression and classification. We apply our method to problems of estimating contamination levels and contaminant source location. © 2007 IEEE.

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TYPE Conference YEAR 2007
OSTIScopus

Dispersion analysis using particle tracking simulations through heterogeneity based on outcrop lidar imagery

Tidwell, Vincent C.; Mckenna, Sean A.

Solute plumes are believed to disperse in a non-Fickian manner due to small-scale heterogeneity and variable velocities that create preferential pathways. In order to accurately predict dispersion in naturally complex geologic media, the connection between heterogeneity and dispersion must be better understood. Since aquifer properties can not be measured at every location, it is common to simulate small-scale heterogeneity with random field generators based on a two-point covariance (e.g., through use of sequential simulation algorithms). While these random fields can produce preferential flow pathways, it is unknown how well the results simulate solute dispersion through natural heterogeneous media. To evaluate the influence that complex heterogeneity has on dispersion, we utilize high-resolution terrestrial lidar to identify and model lithofacies from outcrop for application in particle tracking solute transport simulations using RWHet. The lidar scan data are used to produce a lab (meter) scale two-dimensional model that captures 2-8 mm scale natural heterogeneity. Numerical simulations utilize various methods to populate the outcrop structure captured by the lidar-based image with reasonable hydraulic conductivity values. The particle tracking simulations result in residence time distributions used to evaluate the nature of dispersion through complex media. Particle tracking simulations through conductivity fields produced from the lidar images are then compared to particle tracking simulations through hydraulic conductivity fields produced from sequential simulation algorithms. Based on this comparison, the study aims to quantify the difference in dispersion when using realistic and simplified representations of aquifer heterogeneity.

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TYPE Conference YEAR 2007
OSTI

Markov models and the ensemble Kalman filter for estimation of sorption rates

Mckenna, Sean A.; Vugrin, Kay E.W.; Vugrin, Eric

Non-equilibrium sorption of contaminants in ground water systems is examined from the perspective of sorption rate estimation. A previously developed Markov transition probability model for solute transport is used in conjunction with a new conditional probability-based model of the sorption and desorption rates based on breakthrough curve data. Two models for prediction of spatially varying sorption and desorption rates along a one-dimensional streamline are developed. These models are a Markov model that utilizes conditional probabilities to determine the rates and an ensemble Kalman filter (EnKF) applied to the conditional probability method. Both approaches rely on a previously developed Markov-model of mass transfer, and both models assimilate the observed concentration data into the rate estimation at each observation time. Initial values of the rates are perturbed from the true values to form ensembles of rates and the ability of both estimation approaches to recover the true rates is examined over three different sets of perturbations. The models accurately estimate the rates when the mean of the perturbations are zero, the unbiased case. For the cases containing some bias, addition of the ensemble Kalman filter is shown to improve accuracy of the rate estimation by as much as an order of magnitude.

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TYPE SAND Report YEAR 2007
DOIOSTI

Markov Models and the Ensemble Kalman Filter for Estimation of Sorption Rates

Sandia journal manuscript; Not yet accepted for publication

Vugrin, Eric; Mckenna, Sean A.; Vugrin, Kay E.W.

Non-equilibrium sorption of contaminants in ground water systems is examined from the perspective of sorption rate estimation. A previously developed Markov transition probability model for solute transport is used in conjunction with a new conditional probability-based model of the sorption and desorption rates based on breakthrough curve data. Two models for prediction of spatially varying sorption and desorption rates along a one-dimensional streamline are developed. These models are a Markov model that utilizes conditional probabilities to determine the rates and an ensemble Kalman filter (EKF) applied to the conditional probability method. Both approaches rely on a previously developed Markov-model of mass transfer, and both models assimilate the observed concentration data into the rate estimation at each observation time. Initial values of the rates are perturbed from the true values to form ensembles of rates and the ability of both estimation approaches to recover the true rates is examined over three different sets of perturbations. The models accurately estimate the rates when the mean of the perturbations are zero, the unbiased case. Finally, for the cases containing some bias, addition of the ensemble Kalman filter is shown to improve accuracy of the rate estimation by as much as an order of magnitude.

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TYPE Presentation YEAR 2007
OSTI

Hierarchical probabilistic regionalization of volcanism for Sengan region, Japan

Geotechnical and Geological Engineering

Kulatilake, Pinnaduwa H.S.W.; Park, Jinyong; Balasingam, Pirahas; Mckenna, Sean A.

A 1 km square regular grid system created on the Universal Transverse Mercator zone 54 projected coordinate system is used to work with volcanism related data for Sengan region. The following geologic variables were determined as the most important for identifying volcanism: geothermal gradient, groundwater temperature, heat discharge, groundwater pH value, presence of volcanic rocks and presence of hydrothermal alteration. Data available for each of these important geologic variables were used to perform directional variogram modeling and kriging to estimate geologic variable vectors at each of the 23949 centers of the chosen 1 km cell grid system. Cluster analysis was performed on the 23949 complete variable vectors to classify each center of 1 km cell into one of five different statistically homogeneous groups with respect to potential volcanism spanning from lowest possible volcanism to highest possible volcanism with increasing group number. A discriminant analysis incorporating Bayes' theorem was performed to construct maps showing the probability of group membership for each of the volcanism groups. The said maps showed good comparisons with the recorded locations of volcanism within the Sengan region. No volcanic data were found to exist in the group 1 region. The high probability areas within group 1 have the chance of being the no volcanism region. Entropy of classification is calculated to assess the uncertainty of the allocation process of each 1 km cell center location based on the calculated probabilities. The recorded volcanism data are also plotted on the entropy map to examine the uncertainty level of the estimations at the locations where volcanism exists. The volcanic data cell locations that are in the high volcanism regions (groups 4 and 5) showed relatively low mapping estimation uncertainty. On the other hand, the volcanic data cell locations that are in the low volcanism region (group 2) showed relatively high mapping estimation uncertainty. The volcanic data cell locations that are in the medium volcanism region (group 3) showed relatively moderate mapping estimation uncertainty. Areas of high uncertainty provide locations where additional site characterization resources can be spent most effectively. The new data collected can be added to the existing database to perform future regionalized mapping and reduce the uncertainty level of the existing estimations. © Springer Science+Business Media B.V. 2006.

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TYPE Conference YEAR 2007
ScopusOSTI
Results 101–150 of 196
Results 101–150 of 196