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Abstract Neural networks (NN) have become an important tool for prediction tasks—both regression and classification—in environmental science. Since many environmental-science problems involve life-or-death decisions and policy making, it is crucial to provide not only predictions but also an estimate of the uncertainty in the predictions. Until recently, very few tools were available to provide uncertainty quantification (UQ) for NN predictions. However, in recent years the computer-science field has developed numerous UQ approaches, and several research groups are exploring how to apply these approaches in environmental science. We provide an accessible introduction to six of these UQ approaches, then focus on tools for the next step, namely, to answer the question:Once we obtain an uncertainty estimate (using any approach), how do we know whether it is good or bad?To answer this question, we highlight four evaluation graphics and eight evaluation scores that are well suited for evaluating and comparing uncertainty estimates (NN based or otherwise) for environmental-science applications. We demonstrate the UQ approaches and UQ-evaluation methods for two real-world problems: 1) estimating vertical profiles of atmospheric dewpoint (a regression task) and 2) predicting convection over Taiwan based onHimawari-8satellite imagery (a classification task). We also provide Jupyter notebooks with Python code for implementing the UQ approaches and UQ-evaluation methods discussed herein. This article provides the environmental-science community with the knowledge and tools to start incorporating the large number of emerging UQ methods into their research. Significance StatementNeural networks are used for many environmental-science applications, some involving life-or-death decision-making. In recent years new methods have been developed to provide much-needed uncertainty estimates for NN predictions. We seek to accelerate the adoption of these methods in the environmental-science community with an accessible introduction to 1) methods for computing uncertainty estimates in NN predictions and 2) methods for evaluating such estimates.
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Reference Governors Based on Offline Training of Regression Neural Networks
This paper presents two machine learning-based constraint management approaches based on Reference Governors (RGs). The first approach, termed NN-DTC, uses regression neural networks to approximate the distance to constraints. The second, termed NN-NL-RG, uses regression neural networks to approximate the input-output map of a nonlinear RG. Both approaches are shown to enforce constraints for a nonlinear second order system. NN-NL-RG requires a smaller dataset size as compared to NN-DTC for well-trained neural networks. For systems with multiple constraints, NN-NL-RG is also more computationally efficient than NN-DTC. Finally, promising results are reported by having both approaches implemented on a more complex spacecraft proximity maneuvering and docking application, through simulations.
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- Award ID(s):
- 1931738
- PAR ID:
- 10553302
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-3702-0
- Page Range / eLocation ID:
- 1411 to 1418
- Format(s):
- Medium: X
- Location:
- Honolulu, Oahu, HI, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SMC53992.2023.10394046
