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.
翻译:气候变化对人类构成重大挑战,特别是气候变化对农业的影响。 负责任的AI应该应对这一挑战。 在本文中,我们审视了一种CBR系统(PBI-CBR),该系统旨在通过准确的作物增长预测支持草原管理来帮助可持续的奶牛养殖。随着气候变化,PBI-CBR的历史案例在预测未来草原增长方面变得不那么有用。因此,我们利用数据扩增扩大PBI-CBR,使用反事实方法(来自XAI)专门处理破坏性气候事件。 研究1显示,PBI-CBR往往使用历史极端气候事件(气候异端事件)来预测气候中断期间的草原增长。研究2显示,合成外层在2018年干旱期间作为反事实产生的合成外层,提高了PBICBR的预测准确性。 这项研究还表明,基于实例的反事实方法比基准、约束性指导方法要好。