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Social and Behavioral Sciences Commons™
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- Climate Change (2)
- Machine learning (2)
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- Arctic (1)
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- Climate sciences (1)
- Climate-change impacts (1)
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Articles 1 - 8 of 8
Full-Text Articles in Social and Behavioral Sciences
Assessing Population Exposure To Coastal Flooding Due To Sea Level Rise, Matthew E. Hauer, Dean Hardy, Scott A. Kulp, Valerie Mueller, David J. Wrathall, Peter U. Clark
Assessing Population Exposure To Coastal Flooding Due To Sea Level Rise, Matthew E. Hauer, Dean Hardy, Scott A. Kulp, Valerie Mueller, David J. Wrathall, Peter U. Clark
Faculty Publications
The exposure of populations to sea-level rise (SLR) is a leading indicator assessing the impact of future climate change on coastal regions. SLR exposes coastal populations to a spectrum of impacts with broad spatial and temporal heterogeneity, but exposure assessments often narrowly define the spatial zone of flooding. Here we show how choice of zone results in differential exposure estimates across space and time. Further, we apply a spatio-temporal flood-modeling approach that integrates across these spatial zones to assess the annual probability of population exposure. We apply our model to the coastal United States to demonstrate a more robust assessment …
Cognition-Enhanced Machine Learning For Better Predictions With Limited Data, Florian Sense, Ryan Wood, Michael G. Collins, Joshua Fiechter, Aihua W. Wood, Michael Krusmark, Tiffany Jastrzembski, Christopher W. Myers
Cognition-Enhanced Machine Learning For Better Predictions With Limited Data, Florian Sense, Ryan Wood, Michael G. Collins, Joshua Fiechter, Aihua W. Wood, Michael Krusmark, Tiffany Jastrzembski, Christopher W. Myers
Faculty Publications
The fields of machine learning (ML) and cognitive science have developed complementary approaches to computationally modeling human behavior. ML's primary concern is maximizing prediction accuracy; cognitive science's primary concern is explaining the underlying mechanisms. Cross-talk between these disciplines is limited, likely because the tasks and goals usually differ. The domain of e-learning and knowledge acquisition constitutes a fruitful intersection for the two fields’ methodologies to be integrated because accurately tracking learning and forgetting over time and predicting future performance based on learning histories are central to developing effective, personalized learning tools. Here, we show how a state-of-the-art ML model can …
The Impact Of Climate Change On Virginia's Coastal Areas, Jonathan L. Goodall, Antonio Elias, Elizabeth Andrews, Christopher "Kit" Chope, John Cosgrove, Jason El Koubi, Jennifer Irish, Lewis L. Lawrence Iii, Robert W. Lazaro Jr., William H. Leighty, Mark W. Luckenbach, Elise Miller-Hooks, Ann C. Phillips, Henry Pollard V, Emily Steinhilber, Charles Feigenoff, Jennifer Sayegh
The Impact Of Climate Change On Virginia's Coastal Areas, Jonathan L. Goodall, Antonio Elias, Elizabeth Andrews, Christopher "Kit" Chope, John Cosgrove, Jason El Koubi, Jennifer Irish, Lewis L. Lawrence Iii, Robert W. Lazaro Jr., William H. Leighty, Mark W. Luckenbach, Elise Miller-Hooks, Ann C. Phillips, Henry Pollard V, Emily Steinhilber, Charles Feigenoff, Jennifer Sayegh
Faculty Publications
As part of HJ47/SJ47 (2020), the Virginia General Assembly directed the Joint Commission on Technology and Science (JCOTS) to study the “safety, quality of life, and economic consequences of weather and climate-related events on coastal areas in Virginia.” In pursuit of this goal, the commission was to “accept any scientific and technical assistance provided by the nonpartisan, volunteer Virginia Academy of Science, Engineering, and Medicine (VASEM). VASEM convened an expert study board with representation from the Office of the Governor, planning district commissions in coastal Virginia, The Port of Virginia, the Virginia Economic Development Partnership, state universities, private industry, and …
Per-Pixel Cloud Cover Classification Of Multispectral Landsat-8 Data, Salome E. Carrasco [*], Torrey J. Wagner, Brent T. Langhals
Per-Pixel Cloud Cover Classification Of Multispectral Landsat-8 Data, Salome E. Carrasco [*], Torrey J. Wagner, Brent T. Langhals
Faculty Publications
Random forest and neural network algorithms are applied to identify cloud cover using 10 of the wavelength bands available in Landsat 8 imagery. The methods classify each pixel into 4 different classes: clear, cloud shadow, light cloud, or cloud. The first method is based on a fully connected neural network with ten input neurons, two hidden layers of 8 and 10 neurons respectively, and a single-neuron output for each class. This type of model is considered with and without L2 regularization applied to the kernel weighting. The final model type is a random forest classifier created from an ensemble of …
Year-Independent Prediction Of Food Insecurity Using Classical & Neural Network Machine Learning Methods, Caleb Christiansen, Torrey J. Wagner, Brent Langhals
Year-Independent Prediction Of Food Insecurity Using Classical & Neural Network Machine Learning Methods, Caleb Christiansen, Torrey J. Wagner, Brent Langhals
Faculty Publications
Current food crisis predictions are developed by the Famine Early Warning System Network, but they fail to classify the majority of food crisis outbreaks with model metrics of recall (0.23), precision (0.42), and f1 (0.30). In this work, using a World Bank dataset, classical and neural network (NN) machine learning algorithms were developed to predict food crises in 21 countries. The best classical logistic regression algorithm achieved a high level of significance (p < 0.001) and precision (0.75) but was deficient in recall (0.20) and f1 (0.32). Of particular interest, the classical algorithm indicated that the vegetation index and the food price index were both positively correlated with food crises. A novel method for performing an iterative multidimensional hyperparameter search is presented, which resulted in significantly improved performance when applied to this dataset. Four iterations were conducted, which resulted in excellent 0.96 for metrics of precision, recall, and f1. Due to this strong performance, the food crisis year was removed from the dataset to prevent immediate extrapolation when used on future data, and the modeling process was repeated. The best “no year” model metrics remained strong, achieving ≥0.92 for recall, precision, and f1 while meeting a 10% f1 overfitting threshold on the test (0.84) and holdout (0.83) datasets. The year-agnostic neural network model represents a novel approach to classify food crises and outperforms current food crisis prediction efforts.
A Review Of Energy-For-Water Data In Energy-Water Nexus Publications, Christopher M. Chini, Lauren E. Excell, Ashlynn S. Stillwell
A Review Of Energy-For-Water Data In Energy-Water Nexus Publications, Christopher M. Chini, Lauren E. Excell, Ashlynn S. Stillwell
Faculty Publications
Published literature on the energy-water nexus continues to increase, yet much of the supporting data, particularly regarding energy-for-water, remains obscure or inaccessible. We perform a systematic review of literature that describes the primary energy and electricity demands for drinking water and wastewater systems in urban environments. This review provides an analysis of the underlying data and other properties of over 170 published studies by systematically creating metadata on each study. Over 45% of the evaluated studies utilized primary data sources (data collected directly from utilities), potentially enabling large-scale data sharing and a more comprehensive understanding of global water-related energy demand. …
Workshop Outcomes Report: 1st International Workshop On Seismic Resilience Of Arctic Infrastructure And Social Systems, Majid Ghayoomi, Katharine Duderstadt, Alexander Kholodov, Alexander Shiklomanov, Matthew Turner, Elham Ajorlou
Workshop Outcomes Report: 1st International Workshop On Seismic Resilience Of Arctic Infrastructure And Social Systems, Majid Ghayoomi, Katharine Duderstadt, Alexander Kholodov, Alexander Shiklomanov, Matthew Turner, Elham Ajorlou
Faculty Publications
No abstract provided.
The Traded Water Footprint Of Global Energy From 2010 To 2018, Christopher M. Chini, Rebecca A. M. Peer
The Traded Water Footprint Of Global Energy From 2010 To 2018, Christopher M. Chini, Rebecca A. M. Peer
Faculty Publications
The energy-water nexus describes the requirement of water-for-energy and energy-for-water. The consumption of water in the production and generation of energy resources is also deemed virtual water. Pairing the virtual water estimates for energy with international trade data creates a virtual water trade network, facilitating analysis of global water resources management. In this database, we identify the virtual water footprints for the trade of eleven different energy commodities including fossil fuels, biomass, and electricity. Additionally, we provide the necessary scripts for downloading and pairing trade data with the virtual water footprints to create a virtual water trade network. The resulting …