AI and Machine Learning

Sunday, 28 January 2024: 6:30 PM-8:30 PM
Hall E (The Baltimore Convention Center)
Host: 23rd Annual Student Conference

Papers:
S1
Enhancing Dataset Discovery with Knowledge Graph Link Prediction Techniques
Sean Hughes, GSFC, College Park, MD; and I. Gerasimov, A. Mehrabian, and L. Pham

Handout (253.2 kB)

S2
Detection of Harmful Algal Blooms in Lakes at the Regional Scale Using Satellite Remote Sensing and Machine Learning Techniques
Alastor Sherbatov, Columbia University School of Engineering and Applied Science, New York, NY; Columbia University in the City of New York, New York, NY; NASA, New York, NY; and G. Abreu-Vigil, N. Smirnov, G. Tan, F. Khanom, M. Azarderakhsh, H. Norouzi, and R. Blake

Handout (2.0 MB)

S3
Exploring a Statistical Approach for the Calibration of the NOAA CrIS Sensors Using Machine Learning
Jonathan David Starfeldt, STAR, College Park, MD; Univ. of Wisconsin-Madison, Madison, WI

Handout (2.3 MB)

S4
Satellite-Based Analysis of Water-Color and Dissolved Organic Content within Inland Lakes using Sentinel-2
Aisha Malik, NSF, Brooklyn, NY; and M. Azarderakhsh, H. Norouzi, and R. Blake

Handout (2.6 MB)

S5
Modeling Global Urban Air Temperature Trends Using Machine Learning on Satellite Land Surface Data
Taseen Islam, CUNY Macaulay Honors College, NY, NY; NSF, Brooklyn, NY; and K. Nielsen, S. Sharma, J. A. Grey, A. L. Lofthouse, H. Norouzi, and R. Blake

Handout (2.4 MB)

S6
AI Coastal Upwelling Detection in the Mid-Atlantic Bight
Andrea Alyssa John, Offshore Wind Institute NJEDA, New Brunswick, NJ; and T. Miles

Handout (1.0 MB)

S7
Using Generative and Supervised Neural Networks for Thermal Image Analysis in an Urban Environment
Shaunak Sharma, NASA, New York, NY; and T. Islam, K. Nielsen, J. A. Grey, A. L. Lofthouse, H. Norouzi, and R. Blake

Handout (6.6 MB)

S8
Enhancing Urban Climate Modeling through 3D Quantification of Greenspace using LiDAR and Generative AI
Ethan Peters, NSF, Brooklyn, NY; and H. Norouzi, R. Blake, and P. F. Medina

Handout (1.2 MB)

Downscaling SoilMERGE within an Open Science Framework using Machine Learning

S9
Exploring Data-Driven Equation Discovery for the Modeling of Moisture Flux
Rebecca Z Porter, UCAR, Olathe, KS; and Y. Huang and P. Gentine

Handout (2.6 MB)

S10
Machine Learning Modeling of SWESARR and Lidar Data to Understand How Snow Water Equivalent Changes Spatially and Temporally
Nicholas Justin Pinder, NASA, Ponte Vedra, FL; and A. Joseph, G. Himmele, E. Ofekeze, C. Vuyovich, A. Jain, K. Espada, and J. Conway

S11
Using Machine Learning Approaches for Enhancing Predictability of Southern California Precipitation
Hannah Bao, University of Maryland, College Park, MD; and L. S. Passarella, S. Mahajan, and M. J. Molina
Manuscript (1.1 MB)

S12
Predicting the Summer Rains of the Southwest United States Using Machine Learning
Janelle Rose McManaman, NSF SOARS, Boulder, CO

S13
Predicting the West African Monsoon with a Machine Learning Emulator
Charlotte Merchant, Princeton University, Princeton, NJ; and W. Yang and G. A. Vecchi

S14
Understanding Training Data Components for Excessive Rainfall Machine-Learning Models: A look inside the Unified Flooding Verification System
Mitchell Ryan Lee Green, Central Michigan University, Mount Pleasant, MI; and A. J. Hill and R. S. Schumacher

Handout (1.3 MB)

S15
Predicting Flood Damages using Machine Learning and National Flood Insurance Program Data
Azara Boschee, St. Cloud State University, Sauk Centre, MN; and T. Corringham and W. Hu

Handout (1.3 MB)

S16
Using Machine Learning and XAI techniques for Convective Mode in Future Climate Change Scenarios
Jeremy Malcolm Corner, Northern Illinois University, DeKalb, IL; Northern Illinois Univ., DeKalb, IL; and A. Haberlie, W. S. Ashley, A. C. Michaelis, V. A. Gensini, PhD, CCM, and S. M. Collis

S17
Using Machine Learning Methods to Predict and Understand Severe Weather Over the United States
Eleanor Salm, AI2ES, Norman, OK; Univ. of Wisconsin-Madison, Madison, WI; and M. M. Madsen and A. McGovern

Investigating Additional Predictors to Improve Probabilistic Forecasting of Thunderstorms Using a 2-D Convolutional Neural Network (CNN)

S17A
Agricultural Drought Prediction Using Drought Monitoring Data Between 2001 and 2021 Based on Deep Learning Techniques
Zhanassyl Teleubay, The Ohio State University, Columbus, OH

Artificial intelligence-based investigation of severe thunderstorms producing downbursts in Canada

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
See more of: 23rd Annual Student Conference
<< Previous Session | Next Session >>