Using deep learning to classify potential piping plover habitat along the Upper Missouri River
The U.S. Army Corps of Engineers is required to assess piping plover habitat on the Missouri River annually, per a Biological Opinion. We have been using classification tools to m…
Global food-and-water security-support analysis data (GFSAD) project [2024 INV#WO0000000107073]
1. Landsat-derived rainfed and irrigated area-product of Conterminous United States (LRIP30) 2. Landsat-derived global cropland extent product @ 30 m (LGCEP30) 3. Landsat-derived…
Fire Effects at Whiskeytown National Recreation Area and Lava Beds National Monument
We used random forests to predict the mortality class (live/dead) of lidar derived tree approximate objects and vegetation conditions.
Forest metrics at Redwood National and State Parks
We are developing a convolutional neural network in TensorFlow to predict the presence of treefall gaps based on Sentinel-2 imagery.
Lake Champlain Cyanobacteria Bloom modeling
Developing prediction for harmful algal bloom occurrence
Predicting post-fire tree mortality
We used random forests to select predictor variables for models of individual tree mortality following fire.
Multiple machine-learning estimation of groundwater levels and trends for the regional Mississippi River Valley alluvial aquifer
Improved water management at regional scales.
Experimental Forecast for River Chlorophyll
Forecast total chlorophyll concentrations in streams across various locations, part of the Ecological Forecasting Initiative (EFI) which is a collaboration between USGS and this r…
Accelerating scientific discovery through AI-driven literature synthesis and meta-analysis using large language models [2024 INV#WO0000000201793]]
A team of USGS researchers are conducting a review of literature on drought and its affects on the western United States. Due to a large volume of literature, we are using AI to f…
Storm Induced Erosion Response Network
Tool is used for separation (segmentation) of land and water in images. The resulting mask is used to calculate water levels. Tool will be used to compare to forecasted water leve…
Catalog of stock ponds using machine learning
There are many undocumented stock ponds that retain water for use by farmers and ranchers throughout the landscape in the Dakotas. The stock pond AI model could identify unknown s…
LLM-Assisted Volcanic Alert Monitoring
The primary goal of this project is to automate the monitoring of volcanic alert levels from the public websites of our partner observatories.
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.