Department of the Interior

A team of USGS researchers are conducting a review of literature on drought and its affects on the western United States.

May ultimately enable better hazard assessment, reduces potential disaster costs, and improves the accuracy of public hazard maps and building guidelines.

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.

Machine learning and statistical modeling will be used to leverage region-wide waterbody invasion histories and datasets on the physical, biological, chemical, anthropogenic, and geographic characteristics of these wa...

A machine learning algorithm that utilizes statistics of earthquake waveforms to determine whether an earthquake is large enough to warrant an earthquake early warning alert, with applications to the earthquake early...

This application of machine learning is being used to aggregate complex spatial data to develop a statistical model used to create detailed maps of headwater stream resources and habitat.

bring together various wildlife health data streams across informational domains (i.e., laboratory results, environmental observations, news media, etc.)

It is important that environmental streamflow assessments by water managers consider changes in climate, land use, and water management; this cannot be done effectively without understanding historical variability and...

development of a strategic framework for integrating Energy Resources Program science with traditional information technology related platforms

This research strives to aid in the development of condition-based maintenance (CBM) and predictive maintenance (PdM) tools for Hydroelectric Facilities (Generators and Pumps) by exploring, testing, and developing sof...

Water treatment processes are often complex and maintenance activities present tradeoffs between cost and system performance.

The process for determining the seismic risk for dams is time intensive.

Invasive mussels pose a number of challenges for the continuous operation of water infrastructure.

We have multiple systems with multiple user guides that are 200 plus pages.

Consolidate Resource Management Planning Documents including but not limited to Resource Management Plans and Amendments to create a "current" decisions outputs.

This project would gather various information from various websites/applications after manipulating the inputs on the webpage to get images and store them for human analysis.

A means to predict and prevent failure is at the forefront of operations and maintenance.

Utilize historical datasets of air temperature, precipitation, discharge, and water surface elevation to train a deep learning model to predict discharge and water surface elevation across the Upper Mississippi River...

We used random forests to predict the mortality class (live/dead) of lidar derived tree approximate objects and vegetation conditions.

We are developing a convolutional neural network in TensorFlow to predict the presence of treefall gaps based on Sentinel-2 imagery.

Developing prediction for harmful algal bloom occurrence

Improved water management at regional scales.

There is growing interest in producing critical minerals and energy-related commodities within the United States to reduce dependence on foreign sources.

Increasing access to public lands for recreation starts with effective transportation planning.

Tool is used for separation (segmentation) of land and water in images.

Mapping prairie dog colonies in national is labor intensive and costly.

Estimating metal concentrations in the Animas River near Cedar Hill, NM.

Identifying volcanic deformation in satellite imagery combined with ground-based GNSS

Species Distribution Models for Pectis imberbis, a Rare Plant Species in Southeastern Arizona

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 machine learning methods.

Currently, models of global climate change lack the resolution needed to model the processes that create most of Hawai?i's rainfall.

While global climate models (GCMs) offer climate projections, their coarse spatial resolution does not allow regional characteristics to be captured accurately, which are details crucial for managing risks related to...

Development of a deep learning models to generate earthquake ground motion time series for potential application to Earthquake Early Warning, Operational Aftershock Forecasting, and the National Seismic Hazard Model.

Considerable public dollars will be invested in both tidal marsh restoration and transportation upgrades in the Baylands; yet the combined and interactive effects of SLR and infrastructure changes on wildlife habitat,...

Ticks are one of the most important vectors of disease in North America; however, their presence in desert ecosystems is often underestimated.

To predict stream velocity from streamflow and geographic attributes.

Deep learning methods are being used to improve detection of earthquakes to provide more complete, high-resolution catalogs that are used in research to better understand earthquake occurrence, rupture processes and s...

discrimination among biological radar target types detected by NEXRAD

Classification model identifying soils potentially contaminated by lead from battery recycling

AI leverages the very large water quality dataset from extensive existing in situ data at continuous monitoring stations to develop a remote sensing model with improved accuracy for DOM estimates from satellite images

AI provides the framework for understanding the relationship between optical water quality parameters and non-optical contaminants to develop highly accurate remote sensing models for mercury from satellite images and...

Improve streamflow forecasting predictions in the Willamette River basin.

develop a model that can be used to interpret cell phone images to predict oil in environmental samples

automatically process Normalized Difference Vegetation Index images and come up with a true value of live vegetation and fill in missing data

Accurate prediction of flood and drought impacts requires understanding upstream surface water storage dynamics and storage capacity

The construction of detailed geologic maps requires a lot of manual GIS data input to outline the extent of interpreted geologic features.

better understand the wetting/drying dynamics of small wetlands relevant to amphibians

Safety, expense, observer bias and lack of methodological consistency are rising concerns associated with observer-based surveys, making it imperative to transition to more sustainable methods.

understand how land management treatments affect the probability of reburning

developing new ML metrics for evaluating model performance that work with sparse natural resources, addressing the extreme mathematical sparsity of these resources at the regional scale, and engineering new evidence l...

Literature review and data compilation is the most time-consuming phase of the mineral resource assessment workflow (https://www.usgs.gov/media/images/usgs-mineral-resource-assessment-workflow).

Comment analysis - identifying and binning comments from the public.

Utilize generative AI to generate content for NEPA documentation.

Using optical character recognition and natural language processing, the GLO system will automatically extract text and data elements from scanned land record documents that define mineral and lands ownership.

An OIG draft finding identified a significant number of purchase requests where the "Other" UPCs were used instead of the IT related UPCs that would trigger an IT Approval.

Ensemble regressions to predict suspended sediment, total nitrogen, total phosphorus, algal pigments, and algal cell abundances and image-based estimation of suspended sediment concentration via machine learning used...

The ECOSphere species workflow relies on extracting relevant ecological and biological insights from a vast and continuously growing repository of unstructured documents, currently numbering in the millions.

We are predicting PFAS concentrations in soil across the northeast region

We are building a model (likely random forest or boosted regression tree) to predict PFAS concentrations in groundwater supplies in the US.

We have a limited understanding of how the distribution of an invasive grass has changed over time.

Surficial mass movements (SMMs), such as landslides and rockfalls, have seismic signatures distinct from other routinely-recorded seismic sources like earthquakes and explosions.

Need to accurately predict specific pigment concentrations directly from satellite data

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.

The system predicts the location of seafloor massive sulfide deposits for use as critical mineral indicators.

Improve onshore probabilistic inundation forecasts

Determine shoreline location and change as well as sea ice dynamics from coastal cameras in remote communities

Environmental time-series data may suffer from gaps at a variety of time scales, significantly reducing the number of observations to understand phenomena, identify change, calibrate models, and predict future behavior.

This approach will allow for better identification of PFAS source zones and materials, and will enable better estimation of transport processes