Explore the ongoing and innovative projects the DARSE team is working on, highlighting the efforts of the RSEs towards the projects’ goals.
Goal
Create an AI-enhanced multi-scale computational model and integrate with dense data networks to significantly enhance the predictive capability for short and long-term coastal hazards.
Project Urban and Coastal Flood Modeling
The team willl harness machine learning to create detailed land classification from aerial imagery, LiDAR, and other resources for the State of Delaware. This classification will focus on urban land covers, in particular, various types of impervious surfaces (buildings, driveways, streets, sidewalks, parking lots) and tree cover. This effort will provide critical information for urban/coastal flood modeling and facilitate future groundwater and salinization application.
Project Sub-Model for Settling Velocities of Cohesive Sediment in Region-Scale Modeling of Coastal Processes
Transport of cohesive sediment (mud) in estuaries, wetlands, and saltmarshes is key to land preservation, water quality and ecosystem health. Due to a complex process called flocculation, it is very difficult to determine its settling velocity of cohesive particles. During fall 2024, we will harness machine learning to create an efficient surrogate model to predict time series of floc size distribution subject to environmental forcing using comprehensive training data provided by a detailed numerical model for flocculation dynamics. This effort will provide a highly efficient and reliable tool to be used as a sub-model for settling velocities of cohesive sediment in region-scale modeling of coastal processes.
Goal
Collect new text- and image-data on political speeches, and then analyze these data with transformer-based multi-model topic models to facilitate new theoretical insights into nation-states’ foreign policy priorities, and the subtle manners by which they convey these priorities.
Project Understanding Foreign Policy Priorities Through Multi-Model Topic Models of Foreign Policy Speech Texts and Speech Photographs
The team will be collecting and analyzing social text-as-data and social image-as-data. This includes large scale collection and storage of such text and image data, as well as the subsequent application and scaling of transformer-based models for text- and multi-model content. Substantively, these data encompass transcripts and photos of internationall-oriented political speeches from foreign ministers, heads of state, and related high-level political appointees.
ATHLETICS
Goal
Assist baseball pitchers perform better by interpreting, distilling, and communicating pitch data as actionable information to players and coaches
Machine Learning to Augment Pitcher Performance
By using machine learning models such as Random Forest, the team is able to evaluate efficacy for predicting the “Whiff Rate”, which improves the student atheletes athletic development and assists the couches in understanding their players’ performance.
A goal of this project is to use the results as a “proof of concept” which may be scaled by the athletic department.
PLANT & SOIL SCIENCES
Goal
Automate the daily export of soil greenhouse gas (GHG) data from instruments to a remote server, ensuring seamless data flow for real-time access and analysis as well as ensure the accuracy, reliability and usability of the data collected through automated chambers.
Automated Soil GHG Data Quality Assurance and Quality Control System
Reduces manual effort through automated data validation, increasing the overall efficienty of data collection and processing. The team has identified gaps in data due to missing recordings, sensor faultures, or power outages and are developing an automated Python-based QC/QA framework to validate data. There are implemented checks for missing data, range violations, and identifying outliers.