Reducing bias and quantifying uncertainty in watershed flux estimates: the R package loadflex

Many ecological insights into the function of rivers and watersheds emerge from quantifying the flux of solutes or suspended materials in rivers. Numerous methods for flux estimation have been described, and each has its strengths and weaknesses. Currently, the largest practical challenges in flux estimation are to select among these methods and to implement or apply whichever method is chosen. To ease this process of method selection and application, we have written an R software package called loadflex that implements several of the most popular methods for flux estimation, including regressions, interpolations, and the special case of interpolation known as the period-weighted approach. Our package also implements a lesser-known and empirically promising approach called the “composite method,” to which we have added an algorithm for estimating prediction uncertainty. Here we describe the structure and key features of loadflex, with a special emphasis on the rationale and details of our composite method implementation. We then demonstrate the use of loadflex by fitting four different models to nitrate data from the Lamprey River in southeastern New Hampshire, where two large floods in 2006–2007 are hypothesized to have driven a long-term shift in nitrate concentrations and fluxes from the watershed. The models each give believable estimates, and yet they yield different answers for whether and how the floods altered nitrate loads. In general, the best modeling approach for each new dataset will depend on the specific site and solute of interest, and researchers need to make an informed choice among the many possible models. Our package addresses this need by making it simple to apply and compare multiple load estimation models, ultimately allowing researchers to estimate riverine concentrations and fluxes with greater ease and accuracy

Using Data Science and Predictive Analytics to Understand 4-Year University Student Churn

The purpose of this study was to discover factors about first-time freshmen that began at one of the six 4-year universities in the former Tennessee Board of Regents (TBR) system, transferred to any other institution after their first year, and graduated with a degree or certificate. These factors would be used with predictive models to identify these students prior to their initial departure. Thirty-four variables about students and the institutions that they attended and graduated from were used to perform principal component analysis to examine the factors involved in their decisions. A subset of 18 variables about these students in their first semester were used to perform principal component analysis and produce a set of 4 factors that were used in 5 predictive models. The 4 factors of students who transferred and graduated elsewhere were “Institutional Characteristics,” “Institution’s Focus on Academics,” “Student Aptitude,” and “Student Community.” These 4 factors were combined with the additional demographic variables of gender, race, residency, and initial institution to form a final dataset used in predictive modeling. The predictive models used were a logistic regression, decision tree, random forest, artificial neural network, and support vector machine. All models had predictive power beyond that of random chance. The logistic regression and support vector machine models had the most predictive power, followed by the artificial neural network, random forest, and decision tree models respectively

Advancing Bridge Technology, Task 10: Statistical Analysis and Modeling of US Concrete Highway Bridge Deck Performance -- Internal Final Report

Concrete highway bridge deck repairs represent the highest expense associated with bridge maintenance cost. In order to optimize such activities and use the available monies effectively, a solid understanding of the parameters that affect the performance of concrete bridge decks is critical. The National Bridge Inventory (NBI), perhaps the single-most comprehensive source of bridge information, gathers data on more than 600,000 bridges in all fifty states, the District of Columbia, and the Commonwealth of Puerto Rico. Focusing on concrete highway bridge deck performance, this research developed a nationwide database based on NBI data and other critical parameters that were computed by the authors, referred to as the Nationwide Concrete Highway Bridge Deck Performance Inventory (NCBDPI) database. Additionally, two performance parameters were computed from the available concrete bridge deck condition ratings (CR): Time-in-condition rating (TICR) and deterioration rate (DR). Following the aggregation of all these parameters in the NCBDPI database, filtering, and processing were performed. In addition to a basic prescriptive analysis, two types of advanced analysis were applied to the new dataset. First, binary logistic regression was applied to a subset of the data consisting of the highest and lowest DR. Second, a Bayesian survival analysis was performed on the TICR considering censored data. Through the analyses it was possible to show which parameters influence deck performance and create tools that can help agencies and bridge owners make better decisions regarding concrete bridge deck preservation

European Union Timber Regulation Impact on International Timber Markets

The trade of illegal timber, often from illegal logging, has severe environmental, social and economic consequences. The EU’s response to this problem came with the Forest Law Enforcement, Governance and Trade (FLEGT) Action Plan, with its specific goal to end illegal logging, thereby improving sustainability of forest resources. In March 2013, an additional step was taken by implementing the EU Timber Regulation (EUTR). The EUTR requires proof of timber’s origin and legality to ensure that no illegal timber is imported into the EU. To this end the EU intends to block imports of any wood or wood product which comes from unknown sources. Certification of sustainable forest management will help EU importers minimize risk, which is an essential part of their required due diligence system. Monitoring organizations are established to assist trade associations and businesses to construct comprehensive due diligence systems. National competent authorities are designated to follow the trade of the new FLEGT-licensed timber and timber products. In the first year of the EUTR there are positive impacts, of which the most important is awareness of the disastrous situation with illegal logging, driven by exports of illegal timber. Another positive development is tropical timber exporters documenting the legality of their wood exports. Yet another positive feature is establishment of due diligence systems by EU importers. However, there are considerable problems for ensuring legal trade; for example the lack of comprehensive documentation of origin and legality. Analysis of recent trends establishes changes in the European timber trade in terms of sourcing, substitution, diversion to less-demanding countries. Short-term forecasts of market trends and changes will enable further policy assessment to achieve the objectives of improved legality in international timber markets.JRC.H.3-Forest Resources and Climat

Student-centric Model of Learning Management System Activity and Academic Performance: from Correlation to Causation

In recent years, there is a lot of interest in modeling students' digital traces in Learning Management System (LMS) to understand students' learning behavior patterns including aspects of meta-cognition and self-regulation, with the ultimate goal to turn those insights into actionable information to support students to improve their learning outcomes. In achieving this goal, however, there are two main issues that need to be addressed given the existing literature. Firstly, most of the current work is course-centered (i.e. models are built from data for a specific course) rather than student-centered; secondly, a vast majority of the models are correlational rather than causal. Those issues make it challenging to identify the most promising actionable factors for intervention at the student level where most of the campus-wide academic support is designed for. In this paper, we explored a student-centric analytical framework for LMS activity data that can provide not only correlational but causal insights mined from observational data. We demonstrated this approach using a dataset of 1651 computing major students at a public university in the US during one semester in the Fall of 2019. This dataset includes students' fine-grained LMS interaction logs and administrative data, e.g. demographics and academic performance. In addition, we expand the repository of LMS behavior indicators to include those that can characterize the time-of-the-day of login (e.g. chronotype). Our analysis showed that student login volume, compared with other login behavior indicators, is both strongly correlated and causally linked to student academic performance, especially among students with low academic performance. We envision that those insights will provide convincing evidence for college student support groups to launch student-centered and targeted interventions that are effective and scalable.Comment: 43 pages, 9 figures, 18 tables, Journal of Educational Data Mining (Initial Submission

Uncertainty assessment of spatial soil information

Uncertainty is present in our daily lives. It affects our decisions on what to do. The weather forecast might tell us that there is a 60% chance that it will rain: we take umbrellas. If it says that the chance of rain is only 10% we might decide to leave our umbrellas at home and risk getting wet. More seriously, farmers want to know the likelihood of disease in their crops and the deficiencies in plant nutrients in the soil. These are matters that affect profit and loss of farm business. Agencies responsible for public health and environmental protection need to weigh the risk of doing nothing in the face of uncertain threats against the cost of acting unnecessarily to counter them when the threats are almost non-existent. There are many examples of decision making problems involving uncertain soil information. They include the remediation of polluted soil, the prevention of soil erosion, and the mitigation of pesticide leaching. They are practical matters, not purely academic exercises in statistics. All measurements of soil properties (and other environmental variables) contain error in the sense that they depart from the true values. That error arises from imperfections in the analytical instruments, from the people who use them and from errors that occur during the processing of the recorded data to make them suitable for storage in information databases. Short-range spatial variation is another source of error, given that soil samples are never returned to where they were taken and sampling locations have positional error. Soil taken from location s and analysed in the laboratory might differ substantially from the soil at location s + h, even if |h| is as small as a few decimeters. Composite soil sampling can diminish these differences, but some error inevitably persists because even such a composite is still only a sample of all the soil at that site. All this means that we can never be sure about the true state of the soil: we, the producers and users of soil information, are to some extent uncertain. Uncertainty tends to increase when measurements of basic soil properties are used to obtain derived ones via pedotransfer functions or mechanistic models of dynamic soil processes, for example. Interpolation from measurements to create maps of soil properties adds to the errors of measurement and so too increases uncertainties. We must conclude that considerable uncertainty is often associated with the information that is stored in soil databases and presented in various forms, including maps. This does not mean that the information is of no value; uncertainty is not the same as ignorance. In many cases we do know a great deal about the soil, but we must also acknowledge that the information is not perfect. Some numerical expression of the uncertainty is important because it is needed to determine whether the information is sufficiently accurate for the purpose that a user has in mind. Soil data of too poor a quality might lead to flawed decisions with serious undesirable consequences, both economic and environmental. For instance, the European legislation on the use of pesticides in agriculture depends crucially on the leaching potential of these substances to the ground- and surface-water, which in turn depends importantly on soil properties. In these circumstances users should be aware of the quality of the soil information so that they can be sure that it is sufficiently reliable for their purposes. Ideally they should account for the uncertainty of the information when making their decisions. This chapter (i) provides a statistical definition of uncertainty in soil information; (ii) extends this definition to uncertainty in spatial soil information; (iii) reviews methods that are used to quantify uncertainty in soil information, while paying attention to different sources of uncertainty; (iv) shows how uncertainty in soil information propagates through subsequent analyses; and (v) explains how uncertainty information can be used in decision making. It focuses on the quantification of uncertainty of soil properties that are measured and recorded on continuous scales: properties such as pH, particle-size distribution, and soil organic matter content. The chapter also addresses uncertainty of categorical variables, such as soil type and diagnostic properties recorded as present or absent, i.e. binary variables. It begins with defining uncertainty in a single soil measuremen

Performance Based Design and Machine Learning in Structural Fire Engineering: A Case for Masonry

The volatile and extreme nature of fire makes structural fire engineering unique in that the load actions dictating design are intense but not geographically or seasonally bound. Simply, fire can break out anywhere, at any time, and for any number of reasons. Despite the apparent need, fire design of structures still relies on expensive fire tests, complex finite element simulations, and outdated procedures with little room for innovation. This thesis will make a case for adopting the principles of performance-based design and machine learning in structural fire engineering to simplify the process and promote the consideration of fire in all structural engineering applications. This thesis begins with an overview of relevant topics, providing context and a frame of reference for the coming chapters. The first section of this thesis argues for the adoption of performance-based design for the structural fire design of buildings, as obtained through a comprehensive and much needed literature review. The second half of this thesis revolves around the application of performance-based design and simple machine learning in our field. An Excel file accompanies this thesis as an easy-to-use tool to encourage the consideration of fire criteria in masonry projects, focusing not on how heat affects the material-level properties but rather on how those effects accumulate to affect the final design requirements. An outline for the development of a coding-free machine learning model capable of predicting failure of unreinforced masonry structural elements exposed to elevated temperatures including its abilities and limitations, is presented. The thesis concludes with a summary of the above information and the potential for related project scopes in the future

2018 Faculty Excellence Showcase, AFIT Graduate School of Engineering & Management

Excerpt: As an academic institution, we strive to meet and exceed the expectations for graduate programs and laud our values and contributions to the academic community. At the same time, we must recognize, appreciate, and promote the unique non-academic values and accomplishments that our faculty team brings to the national defense, which is a priority of the Federal Government. In this respect, through our diverse and multi-faceted contributions, our faculty, as a whole, excel, not only along the metrics of civilian academic expectations, but also along the metrics of military requirements, and national priorities