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Found 12 papers, 3 papers with code
A Tri-Level Optimization Model for Interdependent Infrastructure Network Resilience Against Compound Hazard Events
Here, our notional operator may choose proxy actions to operate an interdependent system comprised of fuel terminals and gas stations (functioning as supplies) and a transportation network with traffic flow (functioning as demand) to minimize unmet demand at gas stations.
CDA: Contrastive-adversarial Domain Adaptation
Recent advances in domain adaptation reveal that adversarial learning on deep neural networks can learn domain invariant features to reduce the shift between source and target domains.
Robust Causality and False Attribution in Data-Driven Earth Science Discoveries
Simulated data, and observations in climate and ecohydrology, suggest the robustness and consistency of this approach.
Climate-mediated shifts in temperature fluctuations promote extinction risk
When integrated into empirically-parameterized mathematical models that simulate the dynamical and cumulative effects of thermal stress on the performance of 38 global ectotherm species, the projected spatiotemporal changes in temperature fluctuations are expected to give rise to complex regional changes in population abundance and stability over the course of the 21st century.
Deep Transfer Learning on Satellite Imagery Improves Air Quality Estimates in Developing Nations
Urban air pollution is a public health challenge in low- and middle-income countries (LMICs).
Explainable deep learning for insights in El Niño and river flows
The El Ni\~no Southern Oscillation (ENSO) is a semi-periodic fluctuation in sea surface temperature (SST) over the tropical central and eastern Pacific Ocean that influences interannual variability in regional hydrology across the world through long-range dependence or teleconnections.
Bayesian Deep Learning Hyperparameter Search for Robust Function Mapping to Polynomials with Noise
However, our understanding of how to design Bayesian Deep Learning (BDL) hyperparameters, specifically, the depth, width and ensemble size, for robust function mapping with uncertainty quantification, is still emerging.
Machine Learning for Robust Identification of Complex Nonlinear Dynamical Systems: Applications to Earth Systems Modeling
In climate and earth systems models, while governing equations follow from first principles and understanding of key processes has steadily improved, the largest uncertainties are often caused by parameterizations such as cloud physics, which in turn have witnessed limited improvements over the last several decades.
A framework for deep learning emulation of numerical models with a case study in satellite remote sensing
A difficult test for deep learning-based emulation, which refers to function approximation of numerical models, is to understand whether they can be comparable to traditional forms of surrogate models in terms of computational efficiency while simultaneously reproducing model results in a credible manner.
Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning
Furthermore, we find that the lognormal distribution, which can handle skewed distributions, produces quality uncertainty estimates at the extremes.
DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution
The impacts of climate change are felt by most critical systems, such as infrastructure, ecological systems, and power-plants.
Intercomparison of Machine Learning Methods for Statistical Downscaling: The Case of Daily and Extreme Precipitation
Statistical downscaling of global climate models (GCMs) allows researchers to study local climate change effects decades into the future.
