Uncertainties in the Transient Capture-Zone Estimates of Groundwater Supply Wells

Capture zones of water-supply wells are a widely used analysis tool for protection of groundwater resources. Transient analyses of capture zones provide a more complete assessment than the commonly applied steady-state analyses. Previously, we have demonstrated that advection-only analyses can produce biased transient capture-zone estimates. Therefore, it is important to consider the dispersion of contaminant plumes. Here, we extend our study to incorporate temporal and spatial distribution in the contaminant sources and their respective uncertainties. Our analysis indicates that the capture-zone estimates can be very sensitive to the transients in the contaminant releases. Even relatively small uncertainties in the contaminant source, when combined with transient flow effects associated with natural variability of gradients or water-supply pumping, can cause significant uncertainties in the capture-zone estimates. This conclusion has important practical implications. Furthermore, we investigate the impact of uncertainty in the longitudinal and transverse dispersivities on the transient capture estimates

Robust decision analysis for environmental management of groundwater contamination sites

In contrast to many other engineering fields, the uncertainties in subsurface processes (e.g., fluid flow and contaminant transport in aquifers) and their parameters are notoriously difficult to observe, measure, and characterize. This causes severe uncertainties that need to be addressed in any decision analysis related to optimal management and remediation of groundwater contamination sites. Furthermore, decision analyses typically rely heavily on complex data analyses and/or model predictions, which are often poorly constrained as well. Recently, we have developed a model-driven decision-support framework (called MADS; http://mads.lanl.gov) for the management and remediation of subsurface contamination sites in which severe uncertainties and complex physics-based models are coupled to perform scientifically defensible decision analyses. The decision analyses are based on Information Gap Decision Theory (IGDT). We demonstrate the MADS capabilities by solving a decision problem related to optimal monitoring network design.Comment: This paper has been withdrawn by the author due to a crucial sign error in equations 7 and

Nonnegative/binary matrix factorization with a D-Wave quantum annealer

D-Wave quantum annealers represent a novel computational architecture and have attracted significant interest, but have been used for few real-world computations. Machine learning has been identified as an area where quantum annealing may be useful. Here, we show that the D-Wave 2X can be effectively used as part of an unsupervised machine learning method. This method can be used to analyze large datasets. The D-Wave only limits the number of features that can be extracted from the dataset. We apply this method to learn the features from a set of facial images