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.
Classifying CWD-Infected Elk Using Recurrent Neural Networks on GPS Movement Data
Chronic Wasting Disease is difficult and costly to diagnose using traditional biological testing. However, the disease affects an elk's behavior and movement over time. By analyzi…
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…
Random forest models for predicting water quality of inland waters from remotely sensed imagery
Inland waterbodies (i.e. rivers, lakes, reservoirs, ponds, etc.) can face issues of poor water quality which may pose issues to water users, water infrastructure, and ecosystems.…
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…
Enhancing Community and Wildlife Resilience to Sea?Level Rise and Infrastructure Development in the San Pablo Baylands
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 infra…
Biotic and abiotic drivers of the prevalence of a tick and associated vector-borne disease
Ticks are one of the most important vectors of disease in North America; however, their presence in desert ecosystems is often underestimated. The Gulf Coast tick (Amblyomma macul…
Predictive AI applications for estimating water quality constituents as causal factors of harmful algal blooms.
Ensemble regressions to predict suspended sediment, total nitrogen, total phosphorus, algal pigments, and algal cell abundances and image-based estimation of suspended sediment co…
Adaptive Management with AMMonitor
Automated species identification from remote sensed data (images and audio files)
Machine learning for stream velocity prediction
To predict stream velocity from streamflow and geographic attributes.
Improved earthquake detection for research studies [2024 INV#WO0000000108499]
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 earthqua…
Machine Learning for Avalanche Frequency Modeling
The machine learning (Random Forest) was used to identify vegetation characteristics in avalanche paths. This helps determine avalanche return periods in specific avalanche paths.
discrimination among biological radar target types detected by NEXRAD
AI to survey boat traffic
We are using AI to collect boat traffic times when traveling in a specific area by scanning video. Typically, this type of data would be collected by manually watching video and…
target discrimination on portable radar
This application of machine learning is intended as a pilot effort to discriminate among radar target types, specifically between flying animals and precipitation.
Lead attribution model
Classification model identifying soils potentially contaminated by lead from battery recycling
Machine Learning for Rapid Earthquake Magnitude Estimation
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…
An integrated sensor network and data driven approach to satellite remote sensing of Dissolved Organic Matter
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 f…
Machine Learning for automatic fracture mapping and rock identification [2024 INV#WO0000000109499]
Machine learning algorithms are being used to improve detection and characterization of fault surface geometries using the spatial patterns of earthquake locations. We have improv…
PRObability of Streamflow PERmanence (PROSPER models) [2024 INV#WO0000000109074]
predictions of reliable surface flow in streams at regional scales to inform land and water resource decisions related to water availability.
Remote sensing of particulate and filter passing mercury species: models and proxies
AI provides the framework for understanding the relationship between optical water quality parameters and non-optical contaminants to develop highly accurate remote sensing models…
HOTLink: identifying elevated thermal anomalies at volcanoes [2024 INV#WO0000000109217]
Moves beyond threshold-based hotspot detection algorithms using computer vision and CNN for improved detection of weka thermal signals that may be the first indiciation of volcani…