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Results for “Under-reporting”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sources NASA NTRS DOE OSTI

DOE OSTI2025

Misclassification in Workers’ Telecommuting Frequency Choices Using a Generalized Extreme Value Model

Telecommuting frequency is a response variable collected in travel surveys and is, therefore, prone to errors leading to mismeasurements or misclassification. Misclassification of explanatory variables is a common risk when using statistical modeling techniques. We define “misclassification” as a response reported or recorded in the wrong category; for example, a variable is recorded as a 1 when it should be 0. Here, in this context, this study aims to develop a statistical model to analyze telecommuting data which accounts for potential misclassification errors by building on existing literature in econometrics. The empirical analysis was undertaken using the 2017 National Household Travel Survey (NHTS) and the general extreme value (GEV) models available in the literature. Specifically, the frequency of telecommuting days was analyzed using the negative binomial (NB) model recast as the multinomial logit (MNL) model. By nature—and consistent with other studies—NHTS data are prone to errors that can be classified as intentional or unintentional misinformation provided by the person being interviewed. Ignoring these errors while modeling telecommuting frequencies using standard discrete count models can result in biased parameter estimates. The misclassification parameter was calculated for both over-reporting and under-reporting scenarios. The misclassification errors can be as high as 14% over-reported and 10% under-reported, particularly for the neighboring values. Statistical fit comparison between the models shows that models that ignore misclassification have worse data fit and biased parameter estimates with significant policy implications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Experimental uncertainty quantification using templates of expected measurement uncertainties for fast neutron-induced total, capture, and scattering cross sections

Careful experimental uncertainty quantification (UQ) is key for developing trustworthy evaluated nuclear data. Templates to account for missing or under-reported experimental uncertainties were recently developed by the covariance committee of Cross Section Evaluation Working Group (CSEWG). In this work, we illustrate the practical application and limitations of these templates for selected neutron-induced reactions, including (n, tot), (n, γ), and (n, xn) in the fast energy range, to illustrate their use in data analyses for nuclear data evaluations. We show that while the templates provide consistent framework, proper implementation still requires detailed knowledge of experimental conditions and careful treatment of nonlinear effects in cross section derivation. Case studies highlight how template-assisted UQ improves consistency with previous evaluations such as ENDF/B and reveals open challenges in propagating uncertainties across different energy regimes. The main contribution of this paper is to connect formal template recommendations with their use in practical evaluation workflows, clarifying both their benefits and current limitations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Structure-aware Initialization via Numerical Continuation and Informed Priors

Scientific machine learning (SciML) often operates in ill-conditioned, weakly identifiable regimes due to limited data or indirect observations. In such settings, optimization and inference are highly sensitive to the starting point, making initialization--often under-reported--a consequential degree of freedom. Random initialization is not a neutral default as it induces an implicit prior over candidate solutions and can systematically bias the result, producing large run-to-run variability. Here, we formalize this view by treating initialization as a hidden confounder in SciML and develop a unifying theory for structure-aware initialization via numerical continuation, constructing warm starts from related problem instances. Across representative tasks, including physics-informed neural networks, maximum likelihood estimation, and variational inference, warm starts have been shown to consistently reduce optimization effort and improve reliability.

Data integrity↗

DOE OSTI2024

Machine learning mathematical models for incidence estimation during pandemics

Accurate estimates of the incidence of infectious diseases are key for the control of epidemics. However, healthcare systems are often unable to test the population exhaustively, especially when asymptomatic and paucisymptomatic cases are widespread; this leads to significant and systematic under-reporting of the real incidence. Here, we propose a machine learning approach to estimate the incidence of a pandemic in real-time, using reported cases and the overall test rate. In particular, we use Bayesian symbolic regression to automatically learn the closed-form mathematical models that most parsimoniously describe incidence. We develop and validate our models using COVID-19 incidence values for nine different countries, confirming their ability to accurately predict daily incidence. Remarkably, despite the differences in epidemic trajectories and dynamics across countries, we find that a single model for all countries offers a more parsimonious description and is more predictive of actual incidence compared to separate models for each country. Our results show the potential to accurately model incidence in real-time using closed-form mathematical models, providing a valuable tool for public health decision-makers.

Fajardo-Fontiveros, Oscar (ORCID:0000000207058972)↗

DOE OSTI2026

2013 New Mexico Mid-Region Travel Survey

The 2013 New Mexico Mid-Region Travel Survey assessed travel behavior patterns to update a travel demand model for the Albuquerque Metropolitan Planning Area, which consists of Bernalillo County, Valencia County, and southern Sandoval County. It includes the cities of Albuquerque, Rio Rancho, Los Lunas, and Belen as well as some tribal lands. The Mid-Region Council of Governments contracted with Westat to conduct the survey, which included the collection of socio-demographic data and a one-day (24-hour) period of household travel behavior collected during weekdays (Monday through Friday). The survey also included a random selection of a 20% subsample of households (1,023 participants) to take part in a wearable global positioning system, technology-based component of the study, which was used to assess the level of trip under-reporting from the self-reported component of the survey.

1Hz data↗

DOE OSTI2026

2014 Southern Nevada Household Travel Survey

The 2014 Southern Nevada Household Travel Survey collected information from residents in the Las Vegas area to update the regional travel demand model and assess travel behavior. The Regional Transportation Commission of Southern Nevada contracted with Westat to conduct the survey. The survey was conducted in two phases—from March to May 2014 and from August to October 2014. Participants provided demographic and travel data (via a travel log). A 10% subsample (1,694 participants) was randomly selected to take part in a wearable global positioning system (GPS) technology-based component of the study, the purpose of which was to assess the level of trip under-reporting in the self-reported travel logs. Participants in the GPS portion of the study were instructed to record trips in their travel logs on the first day only, while passively recording their travel for three full days.

1Hz data↗

NASA NTRS2015

Spatial and Temporal Analysis of a Global Landslide Catalog

Landslide inventories are critical to support investigations ofwhere andwhen landslides have happened andmay occur in the future; however, there is surprisingly little information on the historical occurrence of landslides at the global scale. This paper presents a new publicly available global landslide catalog (GLC), which is based on media reports, online databases, and other sources. This database is currently available at http://ojo-streamer. herokuapp.com/. The 5741 points in the GLC provide a foundation for evaluating spatial and temporal trends in landslide activity from 2007 to 2013. Globally, landslideswere reportedmost frequently from July to September. Most events occurred in Asia, North America and Southeast Asia. In contrast, fewer than 5% of the fatalities were reported in North America, suggesting a significant amount of under-reporting in other regions as well as potential discrepancies between developing and developed regions. Reported landslide events were also compared to satellite-based precipitation estimates fromthe Tropical RainfallMeasuring Mission (TRMM) to evaluate the co-occurrence of extreme precipitation and landslide activity. Of the 3550 points considered in a subset of the GLC, approximately 60% of the reported landslides have daily precipitation exceeding the 95th percentile of precipitation calculated over a 14-year TRMMrecord for the same location. This study also investigated how the recurrence interval of extreme precipitation corresponded to some of the most catastrophic landslide events. In spite of several reporting and cataloging biases, spatial and temporal analysis of the GLC suggests that it is a valuable database for characterizing global patterns of landslide occurrence and evaluating relationships with extreme precipitation at regional and global scales.

Analysis↗

NASA NTRS2020

Evaluation of Modeled Precipitation in Oceanic Extratropical Cyclones Using IMERG

Using the high spatial and temporal resolution precipitation dataset Integrated Multi-satellitE Retrievals for GPM (IMERG), extratropical cyclone precipitation is evaluated in two reanalyses and two climate models. Based on cyclone-centered composites, all four models overestimate precipitation in the western subsiding and dry side of the cyclones, and underestimate the precipitation in the eastern ascending and moist side. By decomposing the composites into frequency of occurrence and intensity (mean precipitation rate when precipitating), the analysis reveals a tendency for all four models to overestimate frequency and underestimate intensity, with the former issue dominating in the western half and the latter in the eastern half of the cyclones. Differences in frequency are strongly dependent on cyclone environmental moisture, while the differences in intensity are strongly impacted by the strength of ascent within the cyclone. There are some uncertainties associated with the observations: IMERG might under-report frozen precipitation and possibly exaggerate rates in vigorously ascending regions. Nevertheless, the analysis suggests that all models produce extratropical cyclone precipitation too often and too lightly. These biases have consequences when evaluating the changes in precipitation characteristics with changes in cyclone properties: the models disagree on the magnitude of the change in precipitation intensity with a change in environmental moisture and in precipitation frequency with a change in cyclone strength. This complicates accurate predictions of precipitation changes in a changing climate.

Catherine M Naud↗