Session 7B Deep Learning Applications for Environmental Science. Part II

Wednesday, 15 January 2020: 8:30 AM-10:00 AM
156A (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; and the Events )
Chair:
Surya Karthik Mukkavilli, Montreal Institute for Learning Algorithms (Mila), Montreal, QC

Papers:
8:30 AM
7B.1
Multisource Data Integration under a Deep Learning Framework to Improve Streamflow Forecast Ability
Dapeng Feng, The Pennsylvania State Univ., Univ. Park, PA; and C. Shen and K. Fang
Recorded Presentation

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 Networks, and Principal Component Analysis Based Approaches to Thunderstorm Prediction
Hamid Kamangir, Texas A&M Univ.-Corpus Christi, Corpus Christi, TX; and P. E. Tissot, W. G. Collins, and S. A. King
Recorded Presentation

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
Recorded Presentation

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

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