Donna M. Rizzo

Characterization of aquifer properties using artificial neural networks: Neural kriging

A method for pattern completion based on the application of artificial neural networks and possessing many operational objectives of the ordinary kriging approach, neural kriging, is developed. A neural kriging (NK) network is described, implemented in a parallelizing algorithm, and applied to develop maps of discrete spatially distributed fields (e.g., log hydraulic conductivity). NK is, in the case of two discrete field values, similar to indicator kriging. It uses a feed-forward counterpropagation training approach because field observations are available and because fast yet reliable results are obtained. NK is data-driven and requires no estimate of a covariance function. The optimal design of the NK network is found to depend on the number of hidden units in a more complex way than expected. The quality of the estimate of each pixel of the NK maps can be presented as well, as in kriging, to help identify areas in which additional information will be most beneficial. A comparison with a reference field shows that the NK network produces unbiased errors relative to sample bias and reproduces the variogram of a quantized random field with reasonable accuracy. Ordinary kriging (OK) followed by quantization can also perform well; however, estimation errors in the variogram selected for use in OK (in this case the range cofficient in particular) must be carefully examined and treated. The NK method can provide multiple realizations of the estimated field, all of which respect observations; hence conditional simulation is demonstrably possible. The combination of simplicity, interpolation, reasonably accurate prediction statistics, ability to provide conditional simulations, and computational speed suggest that artificial neural networks can be useful tools in geohydrology when applied to specific well-defined problems for which they are well suited, such as aquifer characterization.

Design Optimization for Multiple Management Period Groundwater Remediation

A technique for obtaining a (nearly) optimal scheme using multiple management periods has been developed. The method has been developed for very large scale combinatorial optimization problems. Simulated annealing has been extended to this problem. An importance function is developed to accelerate the search for good solutions. These tools have been applied to groundwater remediation problems at Lawrence Livermore National Laboratory (LLNL). A deterministic site-specific engineering-type flow and transport model (based on the public domain code SUTRA) is combined with the heuristic optimization technique. The objective is to obtain the time-varying optimal locations of the remediation wells that will reduce concentration levels of volatile organic chemicals in groundwater below a given threshold at specified areas on the LLNL site within a certain time frame and subject to a variety of realistic complicating factors. The cost function incorporates construction costs, operation and maintenance costs for injection and extraction wells, costs associated with piping and treatment facilities, and a performance penalty for well configurations that generate flow and transport simulations that exceed maximum concentration levels at specified locations. The resulting application reported here comprises a huge optimization problem. The importance function detailed in this paper has led to rapid convergence to solutions. The performance penalty allows different goals to be imposed on different geographical regions of the site; in this example, short-term off-site plume containment and long-term on-site cleanup are imposed. The performance of the optimization scheme and the effects of various trade-offs in management objectives are explored through examples using the LLNL site.

Displacement history of a limestone normal fault scarp, northern Israel, from cosmogenic 36Cl

The abundance of cosmogenic 36 Cl, measured in 41 limestone samples from a 9 m high bedrock fault scarp, allows us to construct the 14 kyr fault displacement history of the Nahef East normal fault, northern Israel (300 m above sea level, N33° latitude). The Nahef East fault is one of a series of fault scarps located along the 700 m high Zurim Escarpment, a major geomorphic feature. Samples at the top of the scarp have the highest nuclide concentrations (79 x 10 4 atoms (g rock) -1 ); samples at the base have the lowest (11 x 10 4 atoms (g rock) -1 ), Using chemical data from the samples, Nahef East fault scarp geometry, and surface and subsurface production rates for the 36 Cl-producing reactions, we have constructed a numerical model that calculates 36 Cl accumulation on a scarp through time, given a series of unique displacement scenarios. The resulting model 36 Cl concentrations are compared to those measured in the scarp samples. Faulting histories that result in a good match between measured and modeled 36 Cl abundances show three distinct periods of fault activity during the past 14 kyr with over 6 vertical meters of motion occurring during a 3 kyr time period in the middle Holocene. Smaller amounts of displacement occurred before and after the period of most rapid motion. The episodic behavior of the Nahef East fault indicates that the average displacement rate of this fault system has varied through time.

Evaluating the efficiency and temporal variation of pilot-scale constructed wetlands and steel slag phosphorus removing filters for treating dairy wastewater

The performance and temporal variation of three hybrid and three integrated, saturated flow, pilot-scale constructed wetlands (CWs) were tested for treating dairy farm effluent. The three hybrid systems each consisted of two CWs in-series, with horizontal and vertical flow. Integrated systems consisted of a CW (horizontal and vertical flow) followed by a steel slag filter for removing phosphorus. Time series temporal semivariogram analyses of measured water parameters illustrated different treatment efficiencies existed over the course of one season. As a result, data were then divided into separate time period groups and CW systems were compared using ANOVA for parameter measurements within each distinct time period group. Both hybrid and integrated CWs were efficient in removing organics; however, hybrid systems had significantly higher performance (p<0.05) during peak vegetation growth. Compared to hybrid CWs, integrated CWs achieved significantly higher DRP reduction (p<0.05) throughout the period of investigation and higher ammonia reduction (p<0.05) in integrated CWs was observed in late summer. Geochemical modeling demonstrates hydroxyapatite and vivianite minerals forming on steel slag likely control the fate of phosphate ions given the reducing conditions prevalent in the system. The model also demonstrates how the wastewater:slag ratio can be adjusted to maximize phosphorus removal while staying at a near-neutral pH.

Characterization of increased persistence and intensity of precipitation in the northeastern United States

We present evidence of increasing persistence in daily precipitation in the northeastern United States that suggests that global circulation changes are affecting regional precipitation patterns. Meteorological data from 222 stations in 10 northeastern states are analyzed using Markov chain parameter estimates to demonstrate that a significant mode of precipitation variability is the persistence of precipitation events. We find that the largest region-wide trend in wet persistence (i.e., the probability of precipitation in 1 day and given precipitation in the preceding day) occurs in June (+0.9% probability per decade over all stations). We also find that the study region is experiencing an increase in the magnitude of high-intensity precipitation events. The largest increases in the 95th percentile of daily precipitation occurred in April with a trend of +0.7 mm/d/decade. We discuss the implications of the observed precipitation signals for watershed hydrology and flood risk.

Unraveling Associations between Cyanobacteria Blooms and In-Lake Environmental Conditions in Missisquoi Bay, Lake Champlain, USA, Using a Modified Self-Organizing Map

Exploratory data analysis on physical, chemical, and biological data from sediments and water in Lake Champlain reveals a strong relationship between cyanobacteria, sediment anoxia, and the ratio of dissolved nitrogen to soluble reactive phosphorus. Physical, chemical, and biological parameters of lake sediment and water were measured between 2007 and 2009. Cluster analysis using a self-organizing artificial neural network, expert opinion, and discriminant analysis separated the data set into no-bloom and bloom groups. Clustering was based on similarities in water and sediment chemistry and non-cyanobacteria phytoplankton abundance. Our analysis focused on the contribution of individual parameters to discriminate between no-bloom and bloom groupings. Application to a second, more spatially diverse data set, revealed similar no-bloom and bloom discrimination, yet a few samples possess all the physicochemical characteristics of a bloom without the high cyanobacteria cell counts, suggesting that while specific environmental conditions can support a bloom, another environmental trigger may be required to initiate the bloom. Results highlight the conditions coincident with cyanobacteria blooms in Missisquoi Bay of Lake Champlain and indicate additional data are needed to identify possible ecological contributors to bloom initiation.

The comparison of four dynamic systems-based software packages: Translation and sensitivity analysis

Abstract Dynamic model development for describing complex ecological systems continues to grow in popularity. For both academic research and project management, understanding the benefits and limitations of systems-based software could improve the accuracy of results and enlarge the user audience. A Surface Wetness Energy Balance (SWEB) model for canopy surface wetness has been translated into four software packages and their strengths and weaknesses were compared based on ‘novice’ user interpretations. We found expression-based models such as Simulink and GoldSim with Expressions were able to model the SWEB more accurately; however, stock and flow-based models such as STELLA, Madonna, and GoldSim with Flows provided the user a better conceptual understanding of the ecologic system. Although the original objective of this study was to identify an ‘appropriate’ software package for predicting canopy surface wetness using SWEB, our outcomes suggest that many factors must be considered by the stakeholders when selecting a model because the modeling software becomes part of the model and of the calibration process. These constraints may include user demographics, budget limitations, built-in sensitivity and optimization tools, and the preference of user friendliness vs. computational power. Furthermore, the multitude of closed proprietary software may present a disservice to the modeling community, creating model artifacts that originate somewhere deep inside the undocumented features of the software, and masking the underlying properties of the model.

Predicting experienced travel time with neural networks: a PARAMICS simulation study

The implementation of intelligent transportation systems (ITS) in recent years has resulted in the development of systems capable of monitoring roadway conditions and disseminating traffic information to travelers in a network. However, the development of algorithms and methodologies specialized in handling large amounts of data for the purpose of real-time control has lagged behind the sensing and communication technological developments in ITS. In this study, data generated by a PARAMICS model of a real-world freeway section are used to develop an artificial neural network (ANN) capable of predicting experienced travel time between two points on the transportation network. Computational experiments demonstrate that the studied ANNs were able to reasonably predict the experienced travel time. Generally, the study shows that the length of the time lag did not have a statistically significant effect on ANN performance, that speed appears to be the most influential input variable, and no statistically significant difference in ANN performance was observed when data from the left lane loop detector was substituted for data from the right lane loop detector.

Ecohealth Interventions Limit Triatomine Reinfestation following Insecticide Spraying in La Brea, Guatemala

In this study, we evaluate the effect of participatory Ecohealth interventions on domestic reinfestation of the Chagas disease vector Triatoma dimidiata after village-wide suppression of the vector population using a residual insecticide. The study was conducted in the rural community of La Brea, Guatemala between 2002 and 2009 where vector infestation was analyzed within a spatial data framework based on entomological and socio-economic surveys of homesteads within the village. Participatory interventions focused on community awareness and low-cost home improvements using local materials to limit areas of refuge and alternative blood meals for the vector within the home, and potential shelter for the vector outside the home. As a result, domestic infestation was maintained at ≤ 3% and peridomestic infestation at ≤ 2% for 5 years beyond the last insecticide spraying, in sharp contrast to the rapid reinfestation experienced in earlier insecticide only interventions.

Dynamic morphometric characterization of local connective tissue network structure in humans using ultrasound

BackgroundIn humans, connective tissue forms a complex, interconnected network throughout the body that may have mechanosensory, regulatory and signaling functions. Understanding these potentially important phenomena requires non-invasive measurements of collagen network structure that can be performed in live animals or humans. The goal of this study was to show that ultrasound can be used to quantify dynamic changes in local connective tissue structure in vivo. We first performed combined ultrasound and histology examinations of the same tissue in two subjects undergoing surgery: in one subject, we examined the relationship of ultrasound to histological images in three dimensions; in the other, we examined the effect of a localized tissue perturbation using a previously developed robotic acupuncture needling technique. In ten additional non-surgical subjects, we quantified changes in tissue spatial organization over time during needle rotation vs. no rotation using ultrasound and semi-variogram analyses.Results3-D renditions of ultrasound images showed longitudinal echogenic sheets that matched with collagenous sheets seen in histological preparations. Rank correlations between serial 2-D ultrasound and corresponding histology images resulted in high positive correlations for semi-variogram ranges computed parallel (r = 0.79, p < 0.001) and perpendicular (r = 0.63, p < 0.001) to the surface of the skin, indicating concordance in spatial structure between the two data sets. Needle rotation caused tissue displacement in the area surrounding the needle that was mapped spatially with ultrasound elastography and corresponded to collagen bundles winding around the needle on histological sections. In semi-variograms computed for each ultrasound frame, there was a greater change in the area under the semi-variogram curve across successive frames during needle rotation compared with no rotation. The direction of this change was heterogeneous across subjects. The frame-to-frame variability was 10-fold (p < 0.001) greater with rotation than with no rotation indicating changes in tissue structure during rotation.ConclusionThe combination of ultrasound and semi-variogram analyses allows quantitative assessment of dynamic changes in the structure of human connective tissue in vivo.