Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Institution
Articles 1 - 3 of 3
Full-Text Articles in Engineering
Enhancing Management Of Built And Natural Water And Sanitation Systems With Data Science, Nelson Da Luz
Enhancing Management Of Built And Natural Water And Sanitation Systems With Data Science, Nelson Da Luz
Doctoral Dissertations
In the age of the data revolution, the civil engineer can enhance the management of infrastructure systems using new techniques focused on data. This dissertation present three studies in which data science approaches are used to enhance management of water and sanitation systems in both the built and natural environments. Chapters 1 and 2 focus on improving methods for data collection relating to water quality monitoring. In Chapter 1, the efficacy of different water quality sampling program designs is evaluated as the programs relate to meeting monitoring goals. Considerations include how timing, location, and distribution system operations can affect monitoring …
A Unified Risk-Based Framework For Assessing Sustainability And Resiliency Of Civil Infrastructure, Thomas Adam Robbins
A Unified Risk-Based Framework For Assessing Sustainability And Resiliency Of Civil Infrastructure, Thomas Adam Robbins
Boise State University Theses and Dissertations
As of February 2019, the National Aeronautics and Space Administration (NASA) has reported since 1880 the average global temperature has increased 1°C, with the warmest year on record being 2016. As the years continue to pass, it is becoming more evident that climate change is occurring, which is known to be a catalyst for climatic weather events. Statistically speaking, these events are more prevalent, and catastrophic exemplified as hurricanes, earthquakes, flooding, and fires. In addition to the increase of potentially catastrophic events, society as a whole has become more conscientious in the use and preservation of natural resources, waste generation, …
Robust Techniques And Applications In Fuzzy Clustering, Amit Banerjee
Robust Techniques And Applications In Fuzzy Clustering, Amit Banerjee
Dissertations
This dissertation addresses issues central to frizzy classification. The issue of sensitivity to noise and outliers of least squares minimization based clustering techniques, such as Fuzzy c-Means (FCM) and its variants is addressed. In this work, two novel and robust clustering schemes are presented and analyzed in detail. They approach the problem of robustness from different perspectives. The first scheme scales down the FCM memberships of data points based on the distance of the points from the cluster centers. Scaling done on outliers reduces their membership in true clusters. This scheme, known as the Mega-clustering, defines a conceptual mega-cluster which …