Hydrologic research is increasingly data and computationally intensive, and often involves hydrologic model simulation and collaboration among researchers. With the development of cyberinfrastructure, researchers are able to improve the efficiency, impact, and effectiveness of their research by utilizing online data sharing and hydrologic modeling functionality. However, further efforts are still in need to improve the capability of cyberinfrastructure to serve the hydrologic science community. This dissertation first presents the evaluation of a physically based snowmelt model as an alternative to a temperature index model to improve operational water supply forecasts in the Colorado River Basin. Then it presents the design of the functionality to share multidimensional space-time data in the HydroShare hydrologic information system. It then describes a web application developed to facilitate input preparation and model execution of a snowmelt model and the storage of these results in HydroShare. The snowmelt model evaluation provided use cases to evaluate the cyberinfrastructure elements developed. This research explored a new approach to advance operational water supply forecasts and provided potential solutions for the challenges associated with the design and implementation of cyberinfrastructure for hydrologic data sharing and modeling.

Ekonomi och prognostisering är en stor utmaning för alla företag och en viktig pusselbit för lönsamheten i byggindustrin. Digitaliseringen har underlättat kontakten mellan entreprenör och projektledning. Med det tryck som digitaliseringen och av andra konkurrerande företag medför är det viktigt att kunna ställa realistiska prognoser med rätta förutsättningar. Detta för att sedan ta vara på utvinna erfarenheter och ha dem i beaktande vid projektering av kommande projekt. Eftersom en väl utförd prognos från varje projekt medför ett underlag till de framtida besluten inom ett företag är det viktigt att hitta och identifiera risker och möjligheter för varje projekt. Bakgrunden för arbetet är behovet att utreda företagets arbetssätt vid prognoshantering. Arbetet utfördes tillsammans med Svevia och mycket av informationen kommer från det företaget. Studien är baserad på intervjuer samt en enkät. De intervjuade personerna hade olika befattningar inom företaget några från företagsledningen och några från produktionen. Brister i dagens arbetssätt synliggjordes och utreddes. Likaså diskuterades frågor kring utbildning, programvaror och resurser. Vid enkäten var respondenterna var anställda på företaget. Resultatet redovisas i form av diagram. Resultaten från enkäten och från intervjuerna analyserades och ledde fram till denna slutsats: brister fanns genom: avsaknad av utbildning, tidsbrist, saknaden av ett enhetligt arbetssätt och kunskapsbrist beträffande programvara. Den viktigaste bristen är saknaden av ett enhetligt arbetssätt, Uppföljning och tillvägagångssätt gällande prognoser borde vara lika för alla anställda. / Economy and forecasting are a big challenge for the corporations and an important piece if the puzzle when it comes to profitability in the construction industry. Digitalization of the workplace within the construction industries has made it easier to communicate between the entrepreneur and project management. A big part of the digitalization and the pressure from other companies makes it very important to be able to make realistic forecasts. Also be able to extract the experience from the forecasts and applying it in the future endeavors of the company. Since a well thought out forecast from every project create a basis in which the future decisions will be grounded in, identifying the risks and possibilities of each project and in how they were resolved to minimize losses will be of much importance. This reports background is the need to investigate a uniform approach to forecast management. This thesis was made in collaboration with Svevia and the bulk of the information was derived from their company. The study is based on interviews conducted with employees and a survey. The respondents held different positions within the company where both members of the management were interviewed and from the production. Shortcomings in current method were made visible and investigated. Likewise issues concerning education, software and resources were discussed. The result of the survey was compiled with diagrams where the employees of the company responded to the different questions. Together with the result of the interviews and the survey the analysis and conclusion was formed. The comprehensive flaws can be attributed to the lack of a uniform practice, where the follow up and approaches are the same for all employees.

In today’s world, where air pollution has become a ubiquitous problem, city air is normally monitored. Such monitoring can produce large amounts of data, and this enables the development of statistical and machine learning techniques for modeling and forecasting air quality. However, the complex nature of air pollution makes such data a challenge to fully utilize. To this end, machine learning methods, especially deep neural networks, have in recent years emerged as a promising technology for more accurate predictions of air pollution levels, and the research problem in this work is; To capture and model the complex dynamics of air pollution with machine learning methods, with an emphasis on deep neural networks. Connected to the research problem is the research question; How can machine learning, in particular deep neural networks, be used to forecast air pollution levels and pollution peaks? An emphasis is put on pollution peaks, as these are the episodes when existing forecasting models tend to give the largest prediction errors. In this work, historical data from air monitoring sensors were utilized to train several neural network architectures, as well as a more straightforward multiple linear regression model, for forecasting background levels of nitrogen dioxide in the center of Stockholm. Several evaluation metrics showed that the neural network models outperformed the multiple linear regression model, however, none of the models had the desired structure of the forecast errors, and all models failed to successfully capture sudden pollution peaks. Nevertheless, the results point to an advantage for the more complex neural network models, and further advances in the field of machine learning, together with higher resolution data, have the potential to improve air quality forecasts even more and cross conventional forecasting limits.

<p>This study examines whether directors appointed after a Chief Executive Officer (CEO) assumed office (“co-opted” directors) affect corporate voluntary disclosure. I find evidence that firms issue management earnings forecasts less frequently when directors are co-opted. These results hold even when these directors are considered independent by regulatory definitions. Cross-sectional tests indicate that my results are stronger when firms disclose bad news, provide higher pay to co-opted directors, CEOs have greater ability to withhold disclosure, and co-opt directors early in the CEO’s tenure. I use NASDAQ/NYSE listing requirements as an exogenous shock to board co-option and find that director co-option has a causal link to less voluntary disclosure. I further show that the effect was robust to the effect of CEOs’ disclosure preferences and experience inside the firm. This study suggests that boards that appear independent using the conventional measures may fail to elicit adequate voluntary disclosure to monitor managers. </p>

Artificial intelligence is often believed to have a detrimental effect on employment. However, when reviewing employment forecasts from The U.S. Bureau of Labor Statistics, no information could be found indicating whether they considered the potential impact of artificial intelligence on employment. This thesis aims to examine the relationship between the 2022 employment forecasts and the artificial intelligence exposure measure for occupations developed by Felten, Raj and Seamans (2021) in “Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses” to determine if there is a significant correlation between the two. Using a dataset of 669 observations resulting from matching occupations using their standard occupational classification codes (SOC), a strong correlation was observed. Yet, it remains unclear whether this is due to deliberately accounting for artificial intelligence or any other factors. Nor can it be asserted that this correlation accurately depicts the continuously evolving job market.

<p> This dissertation consists of three essays that evaluate long-term agricultural projections. The first essay focus on evaluating Congressional Budget Office’s (CBO) baseline projection of United States Department of Agriculture (USDA) mandatory farm and nutrition programs. The second essay examine USDA soybean ending stock projections, and the third essay investigate impact of macroeconomic assumptions on USDA’s baseline farm income projections.  We use publicly available data from Congressional Budget Office (CBO) and United States Department of Agriculture (USDA)</p>

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