LAS Theses and Dissertations

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    The application of machine learning algorithms for flood susceptibility assessment for the state of Kansas
    (Wichita State University, 2023-12) Rimal, Prashant; Demissie, Zelalem S.
    Flooding has been a significant problem in the United States (US) over the past century. Since 1996, more than 1,500 flood events have been recorded in Kansas, resulting in more than billions in losses. This project explored the use of machine learning and publicly available data to assess factors affecting flooding and develop a flood susceptibility map for Kansas at multiple resolutions. It aims to explore the major predictor variables or flood-controlling factors and the response of the Stack Generalization across multiple resolutions and scenarios. Six machine learning (ML) algorithms: Logistic Regression (LR); Random Forest (RF); Support Vector Machine (SVM); K-nearest neighbor (KNN); Adaptive Boosting (Ada Boost); Extreme Gradient Boosting (XG Boost) were employed to determine the most important factors influencing the susceptibility of an area to flooding. The learning set for the ML algorithms comprised geospatial datasets of thirteen flood-controlling factors: rainfall, elevation, slope, aspect, flow direction, flow accumulation, Topographic Wetness Index (TWI), distance from the nearest stream, evapotranspiration, land cover, impervious surface, land surface temperature, and hydrologic soil type. A total of 1,528 non-flood inventories were created for two different scenarios, with the only difference being the inclusion of stream buffers for overall analysis. The ML algorithms were compared and used to estimate flood susceptibility for each location in the geodatabase resulting in a flood-susceptibility map for both cases. Overall, testing results showed that the tree-based ensemble algorithms; XGB and RF ML models performed relatively well in both cases over multiple resolutions compared to other models in predicting flooding with an accuracy ranging from 0.82 to 0.97, respectively. Also, variable importance analysis depicted that predictor variables such as distance from the streams, hydrologic soil type, rainfall, elevation, and impervious surfaces significantly affect flood prediction.
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    Evaluating machine learning models using seismic data sets from different settings
    (Wichita State University, 2023-12) Taskinen, Eldon; Demissie, Zelalem S.
    In prior decades the concept of using mathematical methods to predict earthquakes was considered infeasible. Recent advances in machine learning and predictive modeling offer promising avenues to potentially realize earthquake prediction. In order to test the viability of machine learning methods, experiments were made with earthquake datasets from Kansas and Puerto Rico. The two datasets were chosen for the distinct differences in their tectonic settings. Kansas has few major faults, with a largely inactive subsurface, this produced a smaller dataset with a few large clusters. Puerto Rico is complexly faulted, with an extremely active tectonic setting, this produced a larger dataset with a large number of small clusters. In order to test the effectiveness of these two datasets for machine learning and prediction they were run through four different machine learning algorithms including an LSTM model, Bi-LSTM model, Bi-LSTM model with attention, as well as a transformer algorithm. Not only were the four different machine learning methods compared against each other for accuracy but also the datasets as well. Conclusive findings show that the two different data sets favor different processing methods. The Kansas data performs the best with the Bi-LSTM with attention model, while the Puerto Rico data performs the best with the LSTM model. This is likely due to the tectonic settings of the two regions, since the Kansas dataset has less overall data, and earthquakes are concentrated in a few large clusters, while the Puerto Rico data set has a more even distribution.
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    Butler county at a crossroads: What was lost is remembered
    (Wichita State University, 2023-12) Walenta, Suzanne; Price, Jay M., 1969-
    At some point in all communities, large or small, a challenge emerges of how to sustain and grow overtime. This crossroads can manifest itself when faced with a choice or challenge of honoring its past and identity with a perception of rural life that is ever changing. A crossroads can often apply to memory and the moments that we choose to reflect on. However, the nostalgia of memory doesn’t always reflect the complex reality. In Butler County, an observer can witness a microcosm of the national experience - similar events that were tucked away, glossed over or hidden from view. The objective for Kansas Crossroads of Butler County was to move beyond the founder story narrative and unveil the complexities and richness of a rural community. The Butler County Historical Society’s Rural Crossroads/Kansas Crossroads project started out as a “quick” history retelling of selected towns which grew into a longer episodic series named, Kansas Crossroads of Butler County. The foundation was to use the community representative perspective through recorded interviews; use historical photos and documents, newspapers, and books to explore each community and to share stories that people may not be aware of, negative and positive. The result of this series was a version of rural communities that nostalgia often forgets. There are commonalities in each of the fifteen communities explored – farming, ranching, and oil discovery. However, there are many stories that have been hidden away and forgotten. Nostalgia often forgets community struggles with the Klu Klux Klan; white mob violence trigged in the oil boom days; 19th century physical fights and guns at the ready as two towns fight over the county seat; lawlessness and horse thieves. There are also hidden triumphs such as the first all-women jury. Looking back there are lessons revealed not only in lived experience but in how stories are presented.
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    Narratives and the shaping of culture – societal adzes
    (Wichita State University, 2023-12) Reid, Thane; Boynton, T.J.
    This work attempts to define literary fiction in the context of sociological and anthropological principles in order to craft an understanding of how narratives mold and remold individuals and the societies that consist of these individuals in a generational and cyclical process. By drawing on sociological definitions of social technologies as defined by early 20th and 21st century sociological scholars and synthesis of theoretical ideas from anthropological and literary canons, narrative was defined as a form of social technology. Narrative as a social technology is a theoretical outlook which argues narrative contains amorphous symbolic representations of their originating society’s values, which then produce and reproduce meaning and value within that society in a process that shapes societal structure and ideology over time. The application of narrative as a social technology to narratives is carried out through the comparative analysis of two texts in relation to their originating societies. The first analysis, of Homer’s Iliad, investigates the narrative as a social technology through comparative analysis of the epic genre and the prevalence of the text’s impact in contemporary historical records. Additionally, the text is compared to Walcott’s Omeros in order to investigate the modern use of the epic in its relationship to societal change. The second analysis investigates narrative as an explicitly designed example of social technology in Upton Sinclair’s the Jungle, focusing on the text’s role in the creation of the United States Federal Food and Drug Administration and the curtailing of the Chicago meatpacking plants, noting the connection to the narrative presented and the institutional change that stemmed from the works publishing.
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    Exploring the lived experiences of resident pediatricians regarding vaccine hesitancy
    (Wichita State University, 2023-12) Baus, Desiree Sara; Hertzog, Jodie
    Vaccine hesitancy is a phenomenon that is becoming more prevalent in the United States, resulting in the World Health Organization declaring it one of the greatest threats to global health. Previous research has uncovered reasons behind vaccine hesitancy, and in turn, specific strategies suited to countering hesitancy responses. However, little is known about the lived experiences of healthcare professionals when confronted with vaccine hesitant patients. This study fills this gap by examining qualitative data obtained from interviews with ten resident pediatricians enrolled in a program in the midwest to explore experiences with vaccine hesitant patients or families. Interview transcripts were analyzed thematically using both deductive, in line with existing theoretical frameworks, and “in vivo,” or inductive methods based on the emergence of trends and categories. Key themes related to perceptions of the current environment of vaccine hesitancy, resident experiences and processes involved in addressing vaccine hesitancy encounters, as well as training formats and perceived efficacy were identified with the use of qualitative analysis software and were further systematically analyzed through the creation of dashboards to identify relationships between themes. Findings indicate that resident pediatricians respond well to mentor-based vaccine hesitancy training, adopt non-authoritarian tactics to increase vaccine uptake in patients, and do not utilize specific tactics for managing patient cases that are extremely vaccine hesitant. This study provides valuable insights into the challenges resident pediatricians encounter with vaccine hesitancy and calls for more research into the relationship between the themes to better prepare healthcare professionals for managing hesitant patients.
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