使用反事实处理气候变化：在不确定的气候未来中使用数据增强中的反事实预测作物生长
Handling Climate Change Using Counterfactuals: Using Counterfactuals in Data Augmentation to Predict Crop Growth in an Uncertain Climate Future
摘要：
气候变化对人类构成了重大挑战，特别是对农业的影响，这是一个负责任的人工智能应该应对的挑战。在本文中，我们研究了CBR系统（PBI-CBR），旨在通过支持草地管理，通过准确的作物生长预测，以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生长方面变得不那么有用。因此，我们使用数据增强扩展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表明，历史上，极端的气候事件（气候异常情况）往往被PBI-CBR用来预测气候中断期间的草生长。研究2表明，在2018年干旱期间，作为异常值边界上的反事实生成的合成异常值提高了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，约束指导的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
