Modeling and classifying variable width riparian zones utilizing digital elevation models, flood height data, digital soil data and national wetlands inventory : a new approach for riparian zone delineation

Riparian zones are dynamic, transitional ecosystems between aquatic and terrestrial ecosystems with well defined vegetation and soil characteristics. Development of an all-encompassing definition for riparian ecotones, because of their high variability, is challenging. However, there are two primary factors that all riparian ecotones are dependent on: the watercourse and its associated floodplain. Previous approaches to riparian boundary delineation have utilized fixed width buffers, but this methodology has proven to be inadequate as it only takes the watercourse into consideration and ignores critical geomorphology, associated vegetation and soil characteristics. Our approach offers advantages over other previously used methods by utilizing: the geospatial modeling capabilities of ArcMap GIS; a better sampling technique along the water course that can distinguish the 50-year flood plain, which is the optimal hydrologic descriptor of riparian ecotones; the Soil Survey Database (SSURGO) and National Wetland Inventory (NWI) databases to distinguish contiguous areas beyond the 50-year plain; and land use/cover characteristics associated with the delineated riparian zones. The model utilizes spatial data readily available from Federal and State agencies and geospatial clearinghouses. An accuracy assessment was performed to assess the impact of varying the 50-year flood height, changing the DEM spatial resolution (1, 3, 5 and 10m), and positional inaccuracies with the National Hydrography Dataset (NHD) streams layer on the boundary placement of the delineated variable width riparian ecotones area. The result of this study is a robust and automated GIS based model attached to ESRI ArcMap software to delineate and classify variable-width riparian ecotones.

MATURE FINE TAILINGS (MFTs): A STUDY OF COMPRESSIVE STRENGTH AND RHEOLOGICAL PROPERTIES OF ATHABASCA OIL SANDS PETROLEUM MINING WASTE APPLIED IN CONCRETE MIXTURES

This study investigates the compressive properties of concrete incorporating Mature Fine Tailings (MFTs) waste stream from a tar sands mining operation. The objectives of this study are to investigate material properties of the MFT material itself, as well as establish general feasibility of the utilization of MFT material in concrete mixtures through empirical data and visual observations. Investigations undertaken in this study consist of moisture content, materials finer than No. 200 sieve, Atterburg Limits as well as visual observations performed on MFT material as obtained. Control concrete mixtures as well as MFT replacement mixture designs (% by wt. of water) were guided by properties of the MFT material that were experimentally established. The experimental design consists of compression testing of 4”-diameter concrete cylinders of a control mixture, 30% MFT, 50% MFT and 70% MFT replacement mixtures with air-entrainer additive, as well as a control mixture and 30% MFT replacement mixture with no air-entrainer. A total of 6 mixtures (2 control mixtures, 4 replacement mixtures) moist-cured in lime water after 24 hours initial curing were tested for ultimate compressive strength at 7 days and 28 days in accordance to ASTM C39. The test results of fresh concrete material show that the addition of air-entrainer to the control mixture increases slump from 4” to 5.5”. However, the use of MFT material in concrete mixtures significantly decreases slump as compared to controls. All MFT replacement mixtures (30%, 50%, and 70%) with air-entrainer present slumps of 1”. 30% MFT with no air-entrainer presents a slump of 1.5”. It was found that 7-day ultimate compressive stress was not a good predictor of 28-day ultimate compressive stress. 28-day results indicate that the use of MFT material in concrete with air-entrainer decreases ultimate compressive stress for 30%, 50% and 70% MFT replacement amounts by 14.2%, 17.3% and 25.1% respectively.

PROBABILISTIC MODELING OF RAINFALL INDUCED LANDSLIDE HAZARD ASSESSMENT IN SAN JUAN LA LAGUNA, SOLOLÁ, GUATEMALA

The municipality of San Juan La Laguna, Guatemala is home to approximately 5,200 people and located on the western side of the Lake Atitlán caldera. Steep slopes surround all but the eastern side of San Juan. The Lake Atitlán watershed is susceptible to many natural hazards, but most predictable are the landslides that can occur annually with each rainy season, especially during high-intensity events. Hurricane Stan hit Guatemala in October 2005; the resulting flooding and landslides devastated the Atitlán region. Locations of landslide and non-landslide points were obtained from field observations and orthophotos taken following Hurricane Stan. This study used data from multiple attributes, at every landslide and non-landslide point, and applied different multivariate analyses to optimize a model for landslides prediction during high-intensity precipitation events like Hurricane Stan. The attributes considered in this study are: geology, geomorphology, distance to faults and streams, land use, slope, aspect, curvature, plan curvature, profile curvature and topographic wetness index. The attributes were pre-evaluated for their ability to predict landslides using four different attribute evaluators, all available in the open source data mining software Weka: filtered subset, information gain, gain ratio and chi-squared. Three multivariate algorithms (decision tree J48, logistic regression and BayesNet) were optimized for landslide prediction using different attributes. The following statistical parameters were used to evaluate model accuracy: precision, recall, F measure and area under the receiver operating characteristic (ROC) curve. The algorithm BayesNet yielded the most accurate model and was used to build a probability map of landslide initiation points. The probability map developed in this study was also compared to the results of a bivariate landslide susceptibility analysis conducted for the watershed, encompassing Lake Atitlán and San Juan. Landslides from Tropical Storm Agatha 2010 were used to independently validate this study’s multivariate model and the bivariate model. The ultimate aim of this study is to share the methodology and results with municipal contacts from the author's time as a U.S. Peace Corps volunteer, to facilitate more effective future landslide hazard planning and mitigation.

Headward retreat of streams in the Late Oligocene to Early Miocene Swiss Alps

This study uses the widths, the spacing and the grain-size pattern of Oligo/Miocene alluvial fan conglomerates in the central segment of the Swiss Alpine foreland to reconstruct the topographic development of the Alps. These data are analysed with models of longitudinal stream profile development, to propose that the Alpine topography evolved from an early transient state where streams adjusted to rock uplift by headward retreat, to a mature phase where any changes in rock uplift were accommodated by vertical incision. The first stage comprises the time interval between ca 31 Ma and 22 Ma, when the Alpine streams deposited many small fans with a lateral spacing of <30 km in the north Alpine foreland. As the range evolved, the streams joined and the fans coalesced into a few large depositional systems with a lateral spacing of ca 80 to 100 km at 22 Ma. The models used here suggest that the overall elevation of the Alps increased rapidly within <5 Myr. The variability in pebble size increased either due to variations in sediment supply, enhanced orographic effects, or preferentially due to a change towards a stormier palaeoclimate. By 22 Ma, only two large rivers carried material into the foreland fans, suggesting that the major Alpine streams had established themselves. This second phase of stable drainage network was maintained until ca 5 Ma, when the uplift and erosion of the Molasse started and streams were redirected both in the Alps and in the foreland. This study illustrates that sedimentological archives of foreland basins can be used to reconstruct the chronology of the topographic development of mountain belts. It is suggested that the finite elevation of mountainous landscapes is reached early during orogeny and can be maintained for millions of years, provided that erosion is efficient.

ICESat Measurements Reveal Complex Pattern of Elevation Changes on Siple Coast Ice Streams, Antarctica

We compare ICESat data (2003-2004) to airborne laser altimetry data (1997-98 and 1999-2000) to monitor surface changes over portions of Van der Veen (VdVIS), Whillans (WIS) and Kamb ice streams (KIS) in the Ross Embayment of the West Antarctic Ice Sheet. The spatial pattern of detected surface changes is generally consistent with earlier observations. However, important changes have occurred during the past decade. For example, areas on the VdVIS and WIS, where large thinning was detected by the airborne surveys, are now closer to being in balance. The upper trunk of KIS continues to build up with thickening rates reaching 0.4 m/year. Our results provide new evidence that the overall mass balance of the region is becoming more positive, but a significant spatial variability exists. They also demonstrate the potential of ICESat data for detecting spatial patterns of surface elevation change in Antarctica.

Ice-Bed Coupling Beneath and Beyond Ice Streams: Byrd Glacier, Antarctica

Ice sheet thickness is determined mainly by the strength of ice-bed coupling that controls holistic transitions from slow sheet flow to fast streamflow to buttressing shelf flow. Byrd Glacier has the largest ice drainage system in Antarctica and is the fastest ice stream entering Ross Ice Shelf. In 2004 two large subglacial lakes at the head of Byrd Glacier suddenly drained and increased the terminal ice velocity of Byrd Glacier from 820 m yr(-1) to 900 m yr(-1). This resulted in partial ice-bed recoupling above the lakes and partial decoupling along Byrd Glacier. An attempt to quantify this behavior is made using flowband and flowline models in which the controlling variable for ice height above the bed is the floating fraction phi of ice along the flowband and flowline. Changes in phi before and after drainage are obtained from available data, but more reliable data in the map plane are required before Byrd Glacier can be modeled adequately. A holistic sliding velocity is derived that depends on phi, with contributions from ice shearing over coupled beds and ice stretching over uncoupled beds, as is done in state-of-the-art sliding theories.

Elemental and Lead Isotopic Data of Copper Finds from the Singen Cemetery, Germany – a Methodological Approach to Investigate Early Bronze Age Trade Networks

We report a trace element - Pb isotope analytical (LIA) database on the "Singen Copper", a peculiar type of copper found in the North Alpine realm, from its type locality, the Early Bronze Age Singen Cemetery (Germany). What distinguishes “Singen Copper” from other coeval copper types? (i) is it a discrete metal lot with a uniform provenance (if so, can its provenance be constrained)? (ii) was it manufactured by a special, unique metallurgical process that can be discriminated from others? Trace element concentrations can give clues on the ore types that were mined, but they can be modified (more or less intentionally) by metallurgical operations. A more robust indicator are the ratios of chemically similar elements (e.g. Co/Ni, Bi/Sb, etc.), since they should remain nearly constant during metallurgical operations, and are expected to behave homogeneously in each mineral of a given mining area, but their partition amongst the different mineral species is known to cause strong inter-element fractionations. We tested the trace element ratio pattern predicted by geochemical arguments on the Brixlegg mining area. Brixlegg itself is not compatible with the Singen Copper objects, and we only report it because it is a rare instance of a mining area for which sufficient trace element analyses are available in the literature. We observe that As/Sb in fahlerz varies by a factor 1.8 above/below median; As/Sb in enargite varies by a factor of 2.5 with a 10 times higher median. Most of the 102 analyzed metal objects from Singen are Sb-Ni-rich, corresponding to “antimony-nickel copper” of the literature. Other trace element concentrations vary by > 100 times, ratios by factors > 50. Pb isotopic compositions are all significantly different from each other. They do not form a single linear array and require > 3 ore batches that certainly do not derive from one single mining area. Our data suggest a heterogeneous provenance of “Singen copper”. Archaeological information limits the scope to Central European sources. LIA requires a diverse supply network from many mining localities, including possibly Brittany. Trace element ratios show more heterogeneity than LIA; this can be explained either by deliberate selection of one particular ore mineral (from very many sources) or by processing of assorted ore minerals from a smaller number of sources, with the unintentional effect that the quality of the copper would not be constant, as the metallurgical properties of alloys would vary with trace element concentrations.

Mining free-text medical records for companion animal enteric syndrome surveillance.

Large amounts of animal health care data are present in veterinary electronic medical records (EMR) and they present an opportunity for companion animal disease surveillance. Veterinary patient records are largely in free-text without clinical coding or fixed vocabulary. Text-mining, a computer and information technology application, is needed to identify cases of interest and to add structure to the otherwise unstructured data. In this study EMR's were extracted from veterinary management programs of 12 participating veterinary practices and stored in a data warehouse. Using commercially available text-mining software (WordStat™), we developed a categorization dictionary that could be used to automatically classify and extract enteric syndrome cases from the warehoused electronic medical records. The diagnostic accuracy of the text-miner for retrieving cases of enteric syndrome was measured against human reviewers who independently categorized a random sample of 2500 cases as enteric syndrome positive or negative. Compared to the reviewers, the text-miner retrieved cases with enteric signs with a sensitivity of 87.6% (95%CI, 80.4-92.9%) and a specificity of 99.3% (95%CI, 98.9-99.6%). Automatic and accurate detection of enteric syndrome cases provides an opportunity for community surveillance of enteric pathogens in companion animals.

Mining the Ecosystem to Improve Type Inference For Dynamically Typed Languages

Dynamically typed languages lack information about the types of variables in the source code. Developers care about this information as it supports program comprehension. Ba- sic type inference techniques are helpful, but may yield many false positives or negatives. We propose to mine information from the software ecosys- tem on how frequently given types are inferred unambigu- ously to improve the quality of type inference for a single system. This paper presents an approach to augment existing type inference techniques by supplementing the informa- tion available in the source code of a project with data from other projects written in the same language. For all available projects, we track how often messages are sent to instance variables throughout the source code. Predictions for the type of a variable are made based on the messages sent to it. The evaluation of a proof-of-concept prototype shows that this approach works well for types that are sufficiently popular, like those from the standard librarie, and tends to create false positives for unpopular or domain specific types. The false positives are, in most cases, fairly easily identifiable. Also, the evaluation data shows a substantial increase in the number of correctly inferred types when compared to the non-augmented type inference.

Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.

Remote Monitoring of Fish in Small Streams: A Unified Approach Using Pit Tags

Accurate assessments of fish populations are often limited by re-observation or recapture events. Since the early 1990s, passive integrated transponders (PIT tags) have been used to understand the biology of many fish species. Until recently, PIT applications in small streams have been limited to physical recapture events. To maximize recapture probability, we constructed PIT antenna arrays in small streams to remotely detect individual fish. Experiences from two different laboratories (three case studies) allowed us to develop a unified approach to applying PIT technology for enhancing data assessments. Information on equipment, its installation, tag considerations, and array construction is provided. Theoretical and practical definitions are introduced to standardize metrics for assessing detection efficiency. We demonstrate how certain conditions (stream discharge, vibration, and ambient radio frequency noise) affect the detection efficiency and suggest that by monitoring these conditions, expectations of efficiency can be modified. We emphasize the importance of consistently estimating detection efficiency for fisheries applications.