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-CBR’s 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 an outlier-boundary, improve the predictive accuracy of PBI-CBR, during the drought of 2018. This study also shows that an case-based counterfactual method does better than a benchmark, constraint-guided method.
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Notes
- 1.
Note, this is after pre-processing to remove noisy cases (originally, N = 138,970).
- 2.
A unique outlier is a case with an extreme value on any of its weather features.
- 3.
Similar results were found for tests of 2017, though less marked, as that year has fewer disruptive events: PBI-CBRO (MAE = 18.58 kg/DM/ha) did better than PBI-CBREX (MAE = 18.62 kg /DM/ha) without the climate outliers, t(18610) = 1.9, p < 0.05, one-tailed.
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Temraz, M., Kenny, E.M., Ruelle, E., Shalloo, L., Smyth, B., Keane, M.T. (2021). Handling Climate Change Using Counterfactuals: Using Counterfactuals in Data Augmentation to Predict Crop Growth in an Uncertain Climate Future. In: Sánchez-Ruiz, A.A., Floyd, M.W. (eds) Case-Based Reasoning Research and Development. ICCBR 2021. Lecture Notes in Computer Science(), vol 12877. Springer, Cham. https://doi.org/10.1007/978-3-030-86957-1_15
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