| Original language | English |
|---|---|
| Pages (from-to) | 474-478 |
| Number of pages | 5 |
| Journal | Transactions of the American Nuclear Society |
| Volume | 130 |
| Issue number | 1 |
| DOIs | |
| State | Published - 2024 |
| Event | 2024 Annual Conference on Transactions of the American Nuclear Society, ANS 2024 - Las Vegas, United States Duration: Jun 16 2024 → Jun 19 2024 |
Funding
a The manuscript preparation for XYY was supported by the strategic Programmatic Development of the Physical Sciences Directorate of the Oak Ridge National Laboratory (ORNL). The author acknowledges technical support from Ms. Huiying Ren, Huifen Zhou, and Patrick Royer for providing data and use of the code. The programmatic support of the experimental work is from the U. S. Department of Energy (DOE) Nuclear Safety Research and Development (NSR&D) Program. The opinions expressed are solely based on the research results of the author. ORNL is managed by UT-Battelle, LLC, for the DOE under contract number DE-AC05-00OR22725. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).
Keywords
- Exploratory Data Analysis
- Random Forest classification
- machine learning
- meteorological data
- statistical analysis