Session 7B Deep Learning Applications for Environmental Science II

Wednesday, 15 January 2020: 8:30 AM-10:00 AM
156A (Boston Convention and Exhibition Center)

Chair:
Surya Karthik Mukkavilli, Montreal Institute for Learning Algorithms (Mila), Montreal, QC
Papers:
8:30 AM
7B.1
Multi-source Data Integration under a Deep Learning Framework to Improve Streamflow Forecast Ability
Dapeng Feng, Pennsylvania State Univ., Univ. Park, PA; and C. Shen and K. Fang

8:45 AM
7B.2
Using Deep Learning to Detect Atmospheric Rivers Across Climate Datasets and Resolutions
Ankur Mahesh, Lawrence Berkeley National Lab, Berkeley, CA; ClimateAi, San Francisco, CA; and T. A. O'Brien, K. Kashinath, M. Mudigonda, M. Prabhat, C. A. Shields, J. J. Rutz, L. R. Leung, A. E. Payne, F. M. Ralph, M. Wehner, and W. D. Collins

9:00 AM
7B.3
A comparison of Deep Learning, Shallow Neural Network, and Principal Component Analysis based approaches to Thunderstorm Prediction
Hamid Kamangir, Texas A&M University-Corpus Christi, Corpus Christi, TX; and P. E. Tissot, W. G. Collins, and S. A. King

9:15 AM
7B.4
Detecting and Classifying Tornado Damage Utilizing Deep Neural Networks and UAS-based Imagery
Melissa A. Wagner, Arizona State Univ., Tempe, AZ; and Z. Chen, J. Das, R. K. Doe, and R. S. Cerveny

9:30 AM
7B.5
Using Deep Learning to Predict Error Growth in Model Forecasts
Christopher P. Rattray, University of Oklahoma, Norman, OK; and D. B. Parsons

Abstract Submissions Closed



See more of: 19th Conference on Artificial Intelligence for Environmental Science
<< Previous Session | Next Session >>