Government Island, located in the Columbia River approximately 16 km (10 mi) upstream of the confluence with the Willamette River, is a wetland mitigation site prompted by expansion of the southwest quadrant of Portland International Airport. The purpose of the study is to predict water levels in two enclosed lowland areas, Jewit Lake and Southeast Pond, based on levels of the Columbia River, precipitation, and evapotranspiration. Mitigation is intended to convert 1.13 km2 (237 acres) of seasonally flooded wetland to 1.27 km2 (267 acres) of semi-permanently flooded wetland and seasonally flooded wetland. Flooding of the wetland is most likely to occur December through January and May through early June when Columbia River water levels at Government Island exceed 3.6 m (12 ft) m.s.l. Flooding of Jewit Lake occurs through a channel connecting the wetland to the Columbia River. A groundwater model (MODFLOW) was parameterized to simulate the hydrology of the wetland. Observations of the subsurface stratigraphy in 25 soil pits, bucket auger cores, and during installation of water monitoring devices were used to estimate thickness and lateral extent of a confining unit that overlies an aquifer. Climatological data for 1994 and water levels were entered into MODFLOW to calibrate rates of water movement through the subsurface. Periods of drying for Jewit Lake and Southeast Pond were predicted based on precipitation and actual evapotranspiration rates expected to be present in the study area between June and December. Results of groundwater modeling show that Jewit Lake will maintain surface water above 3.6 m (12 ft) in most years. Southeast Pond is expected to dry annually as mitigation is unlikely to change the hydrology of Southeast Pond. Groundwater modeling predicted the types of wetlands present at different elevations by evaluating periods of drying within the wetland using the U.S. Fish and Wildlife Service classification of wetlands method. Results suggest that Jewit Lake will be converted to semipermanently flooded wetland below 3.6 m (12 ft) in elevation. Southeast Pond will remain a seasonally flooded wetland as a result of mitigation.

As the importance of R&D has been growing in economic growth, the accountability and effectiveness of R&D programs are highly emphasized. Especially, in times of economic downturn, the evaluation of performance in a firm is needed to justify R&D investment. In response, various attempts have been made to improve success rates of R&D projects, gain competitive advantage, and achieve a firm's growth in profitability. In particular, in industries where technological innovation is significant, strategic technology planning and R&D capabilities may be the lead ones in defining the dynamic capabilities of a firm. In addition, technology forecasting (TF) in technology planning is a crucial step to follow before developing technologies/products/processes in need. In this regard, researchers have an abiding interest in enhancing methods to forecast emerging technology, while practitioners have a considerable interest in selecting appropriate tools to apply in their field for better forecasting results. Nevertheless, so far it is not well documented how appropriately the current research responds to this need. Thus, a thorough review on TF techniques is conducted to help researchers and practitioners capture methodologies in a tangible way and identify the current trends in the TF arena. Moreover, there is still a lack of clear guidance as to where and how particular TF methods are useful in strategic planning based on technology characteristics as well as the nature of industry. The purpose of this study is to enrich the stream of research on TF activities in a firm for practitioners and researchers, a unique context where TF could lead to technological innovation. This research offers a classification of the approaches, and presents technological, industrial, methodological, and organizational aspects of TF methods that are inherent in TF activities. Furthermore, this study provides empirical evidences to support organizational and managerial implications regarding TF activities associated with technology planning in a firm. Research findings in regimes of technological change suggest insights on technological, organizational, and managerial processes within the firm. On the other hand, research on the effects on business performance of "best practices" of strategic planning, which enable firms to articulate their plans to develop, acquire, and deploy resources for accomplishing firms' financial growth, has so far ignored the roles of strategic technology planning associated with TF. In this regard, this study explores a set of indicators, discusses, and presents the findings from the literature in such a way that they become useful for researchers or managers who are in charge of measuring the R&D performance and business performance from innovation activity. Next, this research tested the hypothetical framework proposed not only to provide a current snapshot of how firms across industries implement best practices in strategic technology planning, but also to improve the effectiveness of strategic planning. The results present the positive linkages between TF, technology planning, and superior business performance. The findings in this research help policy makers, universities, research institutes/national labs, and companies to enhance their decision making process on technology development.

The lack of effective demand forecasting strategies can result in imprecise inventory replenishment, inventory overstock, and unused inventory. The purpose of this single case study was to explore successful demand forecasting strategies that leaders of a small, retail, medical supply business used to increase profitability. The conceptual framework for this study was Winters's forecasting demand approach. Data were collected from semistructured, face-to-face interviews with 8 business leaders of a private, small, retail, medical supply business in the southeastern United States and the review of company artifacts. Yin's 5-step qualitative data analysis process of compiling, disassembling, reassembling, interpreting, and concluding was applied. Key themes that emerged from data analysis included understanding sales trends, inventory management with pricing, and seasonality. The findings of this study might contribute to positive social change by encouraging leaders of medical supply businesses to apply demand forecasting strategies that may lead to benefits for medically underserved citizens in need of accessible and abundant medical supplies.

Traditional disease surveillance systems are instrumental in guiding policy-makers' decisions and understanding disease dynamics. The first study in this dissertation looks at sentinel surveillance network design. We consider three location-allocation models: two based on the maximal coverage model (MCM) and one based on the K-median model. The MCM selects sites that maximize the total number of people within a specified distance to the site. The K-median model minimizes the sum of the distances from each individual to the individual's nearest site. Using a ground truth dataset consisting of two million de-identified Medicaid billing records representing eight complete influenza seasons and an evaluation function based on the Huff spatial interaction model, we empirically compare networks against the existing volunteer-based Iowa Department of Public Health influenza-like illness network by simulating the spread of influenza across the state of Iowa. We compare networks on two metrics: outbreak intensity (i.e., disease burden) and outbreak timing (i.e., the start, peak, and end of the epidemic). We show that it is possible to design a network that achieves outbreak intensity performance identical to the status quo network using two fewer sites. We also show that if outbreak timing detection is of primary interest, it is actually possible to create a network that matches the existing network's performance using 42% fewer sites. Finally, in an effort to demonstrate the generic usefulness of these location-allocation models, we examine primary stroke center selection. We describe the ineffectiveness of the current self-initiated approach and argue for a more organized primary stroke center system. While these traditional disease surveillance systems are important, they have several downsides. First, due to a complex reporting hierarchy, there is generally a reporting lag; for example, most diseases in the United States experience a reporting lag of approximately 1-2 weeks. Second, many regions of the world lack trustworthy or reliable data. As a result, there has been a surge of research looking at using publicly available data on the internet for disease surveillance purposes. The second and third studies in this dissertation analyze Wikipedia's viability in this sphere. The first of these two studies looks at Wikipedia access logs. Hourly access logs dating back to December 2007 are available for anyone to download completely free of charge. These logs contain, among other things, the total number of accesses for every article in Wikipedia. Using a linear model and a simple article selection procedure, we show that it is possible to nowcast and, in some cases, forecast up to the 28 days tested in 8 of the 14 disease-location contexts considered. We also demonstrate that it may be possible in some cases to train a model in one context and use the same model to nowcast or forecast in another context with poor surveillance data. The second of the Wikipedia studies looked at disease-relevant data found in the article content. A number of disease outbreaks are meticulously tracked on Wikipedia. Case counts, death counts, and hospitalization counts are often provided in the article narrative. Using a dataset created from 14 Wikipedia articles, we trained a named-entity recognizer (NER) to recognize and tag these phrases. The NER achieved an F1 score of 0.753. In addition to these counts in the narrative, we tested the accuracy of tabular data using the 2014 West African Ebola virus disease epidemic. This article, like a number of other disease articles on Wikipedia, contains granular case counts and deaths counts per country affected by the disease. By computing the root-mean-square error between the Wikipedia time series and a ground truth time series, we show that the Wikipedia time series are both timely and accurate.

The salinization of groundwater resources is a widespread problem in arid agricultural environments. In Mewat, the amount of solutes dissolved in the water has become too high to use for drinking or agriculture. The only fresh water recharge to this bowl-shaped region is through precipitation, which is focused at the foothills of the mountain. The freshest water is found closest to the mountains and the salinity of the groundwater increases as the distance from the mountains increase. The pumps that supply the region with fresh water are located in the shrinking freshwater zone. Locally-monitored wells show the movement of salinity in the region, as the saline water encroaches upon the freshwater. This study aims to answer the following questions: How long until the region runs out of fresh water? What would it take to have sustainable fresh water supplies? Is it even possible to have sustainable fresh water supplies in this environment? In order to answer these questions, we will quantify potential futures for an arid, groundwater-dependent location in rural India, using numerical groundwater modeling to quantify interactions between human water use, infrastructure, and climate. Outcomes of this modeling study will inform sustainable management of groundwater resources

I study how the properties of management earnings forecasts change after a firm merges with or acquires another company. I find management is more likely to issue a forecast in a merger or an acquisition firm-year than in a non-M&A firm-year. Compared to forecasts issued by the firm in non-M&A periods, the first forecast issued after completing an M&A deal is less likely to be bundled with an earnings announcement and the forecast range is wider, although more likely to be optimistic than non-M&A forecasts. I find the increase in forecast range width and optimism persist in forecasts issued up to the end of the fiscal year but are not present in the initial forecast issued in the subsequent year. Finally, I find variation in M&A experience and M&A type influence management earnings forecast properties. Because prior studies of management forecasts often delete observations containing mergers and acquisitions or simply include the firm’s market-to-book, my study informs researchers about how the properties of management forecasts are impacted by the uncertainty from a merger or an acquisition.

The research presented in this thesis investigates the effect the data collection process has on the results of the economic analysis in pavement management systems. The incorporation of pavement management systems into software packages has enabled local governments to easily implement and maintain an asset management plan. However a general standard has yet to be set, enabling local governments to select from several methods of data collection. In this research, two pavement management system software packages with different data collection methods are analyzed on the common estimated recommended M&R cost provided by their respective economic analysis. The Transportation Asset Management Software (TAMS) software package developed by the Utah LTAP Center at Utah State University consists of a data collection process composed of nine asphalt pavement distress observations. The Micro PAVERTM software package developed by the Army Corps of Engineers consists of a data collection process composed of 20 asphalt pavement distress observations. A Latin-hypercube sample set was input into each software package, as well as actual local government pavement condition data for the City of Smithfield, Utah and the City of Tremonton, Utah. This resulted in six total data sets for analysis, three entered and analyzed in TAMS and three entered and analyzed in Micro PAVERTM. These sample sets were then statistically modeled to determine the effect each distress variable had on the response produced by the economic analysis of estimated recommended M&R costs. Due to the different methodologies of pavement condition data collection, two different statistical approaches were utilized during the sensitivity analysis. The TAMS data sets consisted of a general linear regression model, while the Micro PAVERTM data sets consisted of an analysis of covariance model. It was determined that each data set had varying results in terms of sensitive pavement distresses; however the common sensitive distress in all of the data sets was that of alligator cracking/fatigue. This research also investigates the possibility of utilizing statistically produced models as a direct cost estimator given pavement condition data.

ABSTRACT Hail poses a substantial threat to life and property in the state of Florida. These losses could be minimized through better understanding of the relationships between atmospheric variables that impact hail formation in Florida. Improving hail forecasting in Florida requires analyzing a number of meteorological parameters and synoptic data related to hail formation. NOAA archive data was retrieved to create a database that was used to categorize text files of hail days. The text files were entered into the National Oceanic and Atmospheric Administration Earth System Research Laboratory website to create National Centers for Environmental Prediction/National Center for Atmospheric Research Reanalysis maps of atmospheric variables for Florida hail days as well as days leading to the hail event. These data were then analyzed to determine the relationship between variables that affect hail formation, in general, across different regions and seasons in Florida using Statistical Product and Service Solutions. The reasoning for the differing factors affecting hail formation between regions, seasons and hail sizes were discussed, as well as forecasting suggestions relating to region and month in Florida. The study found that the majority of all hail that occurs in Florida is during the wet season. A low Lifted Index, high Precipitable Water and lower than average Sea Level Pressure, in most cases, is present during hail days in Florida. Furthermore, results show that Vector Wind magnitude increases as hail size increases. Additionally, several atmospheric variables useful to studying hail events, such as Lifted Index, Precipitable Water, Sea Level Pressure, Vector Wind and Temperature have significant correlations with each other depending on the region and season being observed. Strong correlations between low Lifted Index, high Precipitable Water values and the occurrence of hail events are discussed, as well as the relationship between temperature anomalies at various pressure levels and the occurrence of hail events.

Office space oversupply cost Australia billions of dollars during the 1990-92 recession. Australia, the United States, Japan, the U.K., South Africa, China, Thailand, and many other countries have suffered office oversupply cycles. Illiquid untenanted office buildings impair investors capital and cash flows, with adverse effects on macroeconomics, financial institutions, and individuals. This study aims to develop improved methods for medium term forecasting of office market adjustments to inform individual project development decisions and thereby to mitigate office oversupply cycles. Methods combine qualitative research, econometric estimation, system dynamics simulation, and institutional economics. This research operationalises a problem solving research paradigm concept advocated by Ken Lusht. The research is also indebted to the late James Graaskamp, who was successful in linking industry and academic research through time and space specific feasibility studies to inform individual property development decisions. Qualitative research and literature provided a list of contributing causes of office oversupply including random shocks, faulty forecasting methods, fee driven deals, prisoners dilemma game, system dynamics (lags and adjustment times), land use regulation, and capital market issues. Rather than choosing among these, they are all considered to be causal to varying degrees. Moreover, there is synergy between combinations of these market imperfections. Office markets are complex evolving human designed systems (not time invariant) so each cycle has unique historical features. Data on Australian office markets were used to estimate office rent adjustment equations. Simulation models in spreadsheet and system dynamics software then integrate additional information with the statistical results to produce demand, supply, and rent forecasts. Results include ++ / models for rent forecasting and models for analysis related to policy and system redesign. The dissertation ends with two chapters on institutional reforms whereby better information might find application to improve market efficiency.Keywords. Office rents, rent adjustment, office market modelling, forecasting, system dynamics.

Abstract not available