Inference of PFAS precursor compositions
This approach will allow for better identification of PFAS source zones and materials, and will enable better estimation of transport processes
RSCC and TCA projects [2024 INV#WO0000000108017]
Automated identification of coastal features in remote sensing data, reduced analyst time
Lake-wide monitoring and assessment of Great Lakes fisheries with autonomous vehicles and image analysis
Fisheries monitoring and assessment
Improving Prediction Capabilities for Barrier Island Landscape Change
This research uses several AI/ML tools to observe and analyze coastal landscape change at critical habitats along barrier islands. The work employs traditional ML methods (random…
Coastal Change Likelihood: Synthesizing change factors using supervised learning
A supervised machine-learning framework (support vector machine algorithm) is used to predict future decadal-scale coastal change and its primary driver by combining over 20 publi…
Synthesizing mapping and monitoring data to understand fluctuations in prairie dog colony size and densities in Theodore Roosevelt National Park [2024 INV#WO0000000109308]
Mapping prairie dog colonies in national is labor intensive and costly. One of the aims of this project is to develop open-source methods for mapping prairie dog colonies using sa…
USGS Flow Photo Explorer [2024 INV#WO0000000109196]
The Flow Photo Explorer (FPE) is an integrated database, machine learning, and data visualization platform for monitoring streamflow and other hydrologic conditions using timelaps…
Using Graph Neural Networks for development of nonergodic earthquake ground-motion models
May ultimately enable better hazard assessment, reduces potential disaster costs, and improves the accuracy of public hazard maps and building guidelines.
Deep?learning Integrations into NEIC Operations [2024 INV#WO0000000109496]
The AI model improves the rapid detection of earthquakes.
Modeling Rupture Directivity Effects on Ground Motion Using Neural Networks
We use Artificial Neural Networks (ANNs) to more accurately modeling ground motion amplification effects caused by rupture directivity during earthquakes. These effects are comple…
[Un]supervised clustering of [non-]earthquake signals commonly recorded on regional seismic networks
Surficial mass movements (SMMs), such as landslides and rockfalls, have seismic signatures distinct from other routinely-recorded seismic sources like earthquakes and explosions.…
Animas River Metals Surrogate
Estimating metal concentrations in the Animas River near Cedar Hill, NM.
MAGMA-net: Multimodal analysis for geophysical monitoring of activity
Identifying volcanic deformation in satellite imagery combined with ground-based GNSS
AI/ML For Hazard Detection in Austere Environments with Automated or Remotely-Operated Rovers [2024 INV#WO0000000107424]
Identify unique or hazardous ground cover in the field of view of an autonomous robot's camera system
Mapping vegetation classes to understand wildfire fuel conditions
Mapping vegetation classes that can be used to understand wildfire fuel condition across space and through time.
Supervised learning for phycocyanin estimation from Sentinel-2 MSI in inland waters with independent validation
Need to accurately predict specific pigment concentrations directly from satellite data
Feature extraction and characterization for update of The National Map
Reduce time and level of effort for update of The National Map datasets which include the 3D Elevation Program, 3D Hydrography Program, geographic names, and topographic mapping.
Four AI systems using different strategies to identify, classify and locate both volcano-tectonic and non-traditional volcanic earthquakes
Identifying and classifying volcanic earthquakes, which can be very different from traditional tectonic earthquakes. These are addressed with CNN's, hidden Markov models, and pre…
Global Detection of Historical Harmful Algal Blooms via combined Satellite Data and Deep Learning Methods
Create a global-scale geospatial database of HABs occurrences over the past 40 years and compile in-situ measured HAB events and related parameters, remotely sensed data, and mach…
Machine Learning for High-Resolution Downscaling in the Hawaiian Islands
Currently, models of global climate change lack the resolution needed to model the processes that create most of Hawai?i’s rainfall.
Refining Flood Risk Predictions in Hawai?i with Generative Machine Learning
While global climate models (GCMs) offer climate projections, their coarse spatial resolution does not allow regional characteristics to be captured accurately, which are details…
Using High-Resolution Imagery and Artificial Intelligence to Support Climate Change Resilience in Agroforestry Across the Pacific
On remote Pacific islands and outer atolls, agroforestry (i.e., the cultivation and conservation of trees for agriculture) provides food security and income to local communities.…
Predicting from the past - identifying characteristics of invasion-resistant and invasion-prone waterbodies to aid horizon scanning
Machine learning and statistical modeling will be used to leverage region-wide waterbody invasion histories and datasets on the physical, biological, chemical, anthropogenic, and…
Forecasting Earthquake Ground Motion Time Series [2024 INV#WO0000000109733]
Development of a deep learning models to generate earthquake ground motion time series for potential application to Earthquake Early Warning, Operational Aftershock Forecasting, a…