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https://doi.org/10.48550/arxiv...
Article . 2021
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
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Handling Climate Change Using Counterfactuals: Using Counterfactuals in Data Augmentation to Predict Crop Growth in an Uncertain Climate Future

Authors: Temraz, Mohammed; Kenny, Eoin; Ruelle, Elodie; Shalloo, Laurence; Smyth, Barry; Keane, Mark T;

Handling Climate Change Using Counterfactuals: Using Counterfactuals in Data Augmentation to Predict Crop Growth in an Uncertain Climate Future

Abstract

Climate change poses a major challenge to humanity, especially in its impact on agriculture, a challenge that a responsible AI should meet. In this paper, we examine a CBR system (PBI-CBR) designed to aid sustainable dairy farming by supporting grassland management, through accurate crop growth prediction. As climate changes, PBI-CBRs historical cases become less useful in predicting future grass growth. Hence, we extend PBI-CBR using data augmentation, to specifically handle disruptive climate events, using a counterfactual method (from XAI). Study 1 shows that historical, extreme climate-events (climate outlier cases) tend to be used by PBI-CBR to predict grass growth during climate disrupted periods. Study 2 shows that synthetic outliers, generated as counterfactuals on a outlier-boundary, improve the predictive accuracy of PBICBR, during the drought of 2018. This study also shows that an instance-based counterfactual method does better than a benchmark, constraint-guided method.

Comment: 15 pages, 6 figures, 3 tables

Keywords

FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence

27 references, page 1 of 3

1. Rosenzweig, C., Iglesias, A., Yang, X.B., Epstein, P.R., and Chivian, E., Climate Change and U.S. Agriculture. centre for health and the global environment, Harvard Medical School: Boston, MA, USA, (2000)

2. Kenny, E.M., Ruelle, E., Geoghegan, A., Shalloo, L., O'Leary, M., O'Donovan, M., and Keane, M.T.: Predicting grass growth for sustainable dairy farming. In ICCBR-19, pp. 172- 187, Springer, Berlin (2019)

3. Kenny, E.M., Ruelle, E., Geoghegan, A., Shalloo, L., O'Leary, M., O'Donovan, M., Temraz, M., and Keane, M.T.: Bayesian Case-Exclusion for Sustainable Farming. In IJCAI-20 (2020)

4. Keane, M.T. and Smyth, B.: Good counterfactuals and how to find them. In ICCBR-20, pages 163-178. Springer (2020)

5. EU Parliament : Briefing on the EU dairy sector. https://www.europarl.europa.eu/RegData/etudes/BRIE/2018/630345/EPRS_BRI(2018)630345_EN.pdf (2018)

6. Altieri, M.A.: Agroecology: the science of sustainable agriculture. CRC Press (2018).

7. Teagasc: The Dairy Carbon Navigator: Improving carbon efficiency on Irish dairy farms.

8. Ruelle, E., Hennessy, D. and Delaby, L.: Development of the Moorepark St Gilles grass growth model (MoSt GG model). European Journal of Agronomy, 99, pp.80-91 (2018) [OpenAIRE]

9. Hanrahan, L., Geoghegan, A., O'Donovan, M., Griffith, V., Ruelle, E., Wallace, M. and Shalloo, L.: PastureBase Ireland. Computers and Electronics in Agriculture, 136, 193-201 (2017)

10. Hurtado-Uria, C., Hennessy, D., Shalloo, L., O'Connor, D. and Delaby, L.: Relationships between meteorological data and grass growth over time in the south of Ireland. Irish Geography, 46(3), 175-201 (2013) [OpenAIRE]

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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