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Interpretable deep learning for spatial analysis of severe hailstorms
David John Gagne
,
Sue Ellen Haupt
, Douglas W. Nychka
, Gregory Thompson
Machine Integration and Learning for Earth Systems
National Center for Atmospheric Research
Research output
:
Contribution to journal
›
Review article
›
peer-review
154
Scopus citations
Overview
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Dive into the research topics of 'Interpretable deep learning for spatial analysis of severe hailstorms'. Together they form a unique fingerprint.
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Computer Science
Deep Learning Method
100%
Convolutional Neural Network
100%
Deep Learning Model
50%
Prediction Model
25%
Spatial Pattern
25%
Characteristic Curve
25%
Component Analysis
25%
Machine Learning Approach
25%
Logistic Regression
25%
Principal Components
25%
Machine Learning
25%
Learning System
25%
Earth and Planetary Sciences
Hailstorm
100%
Spatial Analysis
100%
Storm
100%
Machine Learning
40%
Updraft
20%
Upper Atmosphere
20%
Wind Shear
20%
Lapse Rate
20%
Principal Component Analysis
20%
Spatiotemporal Distribution
20%
Engineering
Deep Learning Method
100%
Convolutional Neural Network
100%
Learning System
50%
Learning Approach
25%
Principal Components
25%
Characteristic Curve
25%
Component Analysis
25%
Numerical Weather Prediction Model
25%
Wind Shear
25%
Chemical Engineering
Deep Learning Method
100%
Neural Network
100%
Learning System
50%