PNNL

Abstract

A growing number of physical objects with embedded sensors with typically high volume and frequently updated data sets has accentuated the need to develop methodologies to extract useful information from big data for supporting decision making. This study applies a suite of data an-alytics and core principles of data science to characterize near real-time meteorological data with a focus on extreme weather events. To highlight the applicability of this work and make it more accessible from a risk management perspective, a foundation for a software platform with an intuitive graphic user interface (GUI) was developed to access and analyze data from a decom-missioned nuclear production complex operated by the U.S. Department of Energy (DOE). Ex-ploratory data analysis (EDA), involving classical non-parametric statistics, and machine learning (ML) techniques were used to develop statistical summaries and learn characteristic features of key weather patterns and signatures. The new approach and GUI provide key insights into using big data and ML to assist site operation related to safety management strategies for extreme weather events. Specifically, this work offers a practical guide to analyzing long-term meteoro-logical data and highlights the integration of ML and classical statistics to applied risk and deci-sion science.