Big Data Analytics for Long-Term Meteorological Observations at Hanford Site

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.

Smart Installation Weather Warning Decision Support

“…According to Na-Yemeh et al [16], Oklahoma's OK-First weather DSS helped through generating $1.2 million in self-reported cost savings for 12 months. Research studies have developed user interfaces to support decision-makers with statistical summaries [17] and probabilistic forecasts [18] for rapid responses to weather conditions. Another style of DSS allows the user to reason about the impact of data, especially with the rising complexity in both the number and complexity of data sources [19].…”

Adaptive Momentum-Backpropagation Algorithm for Flood Prediction and Management in the Internet of Things

“…Moreover, many models grapple with the complexity of analyzing large-scale data and exhibit limited adaptability, being designed for specific scenarios or locations, which restricts their broad applicability. Traditional approaches to data management often face difficulties in handling the real-time nature of meteorological data and the intricate relationships between various environmental factors [34][35][36][37]. Meteorological data is characterized by its dynamic and time-sensitive nature.…”

A geospatial risk analysis graphical user interface for identifying hazardous chemical emission sources

“…Identifying the potential sources and affected areas due to emissions of hazardous chemicals plays a critical role in risk assessment because it supports decision-making and protects workers and communities from being incidentally exposed to chemical hazards within the identified areas ( TALA, 2001 ). Real-time volatile organic compound (VOC) measurements, such as those from modern analytical instruments like proton transfer mass spectrometer (PTR-MS), coupled with long-term meteorological data ( Zhou et al, 2022 ) are necessary to understand the pollutant concentrations at the emission point. Atmospheric dispersion models are used to perform trajectory simulations of particles formed as a result of VOCs emitted into the air ( Stockie, 2011 ).…”