Abstract
This paper proposes a physics-guided neural network model to predict crop yield and maintain the fairness over space. Failures to preserve the spatial fairness in predicted maps of crop yields can result in biased policies and intervention strategies in the distribution of assistance or subsidies in supporting individuals at risk. Existing methods for fairness enforcement are not designed for capturing the complex physical processes that underlie the crop growing process, and thus are unable to produce good predictions over large regions under different weather conditions and soil properties. More importantly, the fairness is often degraded when existing methods are applied to different years due to the change of weather conditions and farming practices. To address these issues, we propose a physics-guided neural network model, which leverages the physical knowledge from existing physics-based models to guide the extraction of representative physical information and discover the temporal data shift across years. In particular, we use a reweighting strategy to discover the relationship between training years and testing years using the physics-aware representation. Then the physics-guided neural network will be refined via a bi-level optimization process based on the reweighted fairness objective. The proposed method has been evaluated using real county-level crop yield data and simulated data produced by a physics-based model. The results demonstrate that this method can significantly improve the predictive performance and preserve the spatial fairness when generalized to different years.
Original language | English (US) |
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Title of host publication | AAAI-23 Special Tracks |
Editors | Brian Williams, Yiling Chen, Jennifer Neville |
Publisher | AAAI press |
Pages | 14223-14231 |
Number of pages | 9 |
ISBN (Electronic) | 9781577358800 |
DOIs | |
State | Published - Jun 27 2023 |
Event | 37th AAAI Conference on Artificial Intelligence, AAAI 2023 - Washington, United States Duration: Feb 7 2023 → Feb 14 2023 |
Publication series
Name | Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023 |
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Volume | 37 |
Conference
Conference | 37th AAAI Conference on Artificial Intelligence, AAAI 2023 |
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Country/Territory | United States |
City | Washington |
Period | 2/7/23 → 2/14/23 |
Bibliographical note
Publisher Copyright:Copyright © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.