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 analytics 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 Graphical User Interface (GUI) was developed to access and analyze data from a decommissioned nuclear production complex operated by the U.S. Department of Energy (DOE, Richland, USA). Exploratory 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 meteorological data and highlights the integration of ML and classical statistics to applied risk and decision science.
Highlights
Big data is typically defined by high volume and frequently updated data involving a broad range of data types that are often disparate in nature, including structured, semistructured and unstructured sources
Winds with relatively low speed but lasting longer than 3 h occurred more frequently in winter compared to other seasons based on the 10-year meteorological data
If hazardous particles were released during such low wind or stagnant periods, it is less likely they would be transported downwind over longer distances throughout the Hanford site
Summary
Big data is typically defined by high volume and frequently updated data involving a broad range of data types that are often disparate in nature, including structured, semistructured and unstructured sources. One key area where this can be applied most effectively is risk management and planning related to extreme weather events. Extreme events can be characterized by deviation in observed values from long-term norms and can be punctuated by irregular occurrences both spatially and temporally in temperature, wind speed, wind direction, pressure, and other meteorological parameters [1]. These events can be precursors to larger-scale extreme-weather-induced hazards such as drought, wildfire, and flooding. Extreme heat impacts energy demand and consumption, increasing the probability of interruption in daily operation and risks in safeguarding facilities [2]
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