融合生物物理模型与神经网络，提升果树休眠期预测准确性。
Hybrid Phenology Modeling for Predicting Temperature Effects on Tree Dormancy
- 用神经网络修正传统模型结构差异，提升适应性。
- 在日本、韩国和瑞士多地预测开花日期均优于传统与机器学习模型。
- 无需针对新地点重新校准，适合不同品种和区域应用。
生物物理模型在自然与农业环境中对气候-物候关系提供了有价值见解，但不同模型间存在显著结构差异，常需本地校准，导致相似气候情景下预测结果不一致。机器学习方法虽具数据驱动优势，却往往缺乏可解释性且与已有知识脱节。本文提出一种结合传统生物物理模型与神经网络的混合物候模型，用于描述果树休眠过程。在涵盖日本、韩国和瑞士的广泛案例研究中，该模型持续优于传统生物物理模型和机器学习模型，准确预测樱桃树开花时间。神经网络的适应性支持特定树种参数学习，实现无需本地校准即可在新地点稳健泛化。该混合模型兼顾生物物理约束与数据驱动灵活性，为准确且可解释的物候建模提供了新路径。
原文摘要 · Abstract (English)
Biophysical models offer valuable insights into climate-phenology relationships in both natural and agricultural settings. However, there are substantial structural discrepancies across models which require site-specific recalibration, often yielding inconsistent predictions under similar climate scenarios. Machine learning methods offer data-driven solutions, but often lack interpretability and alignment with existing knowledge. We present a phenology model describing dormancy in fruit trees, integrating conventional biophysical models with a neural network to address their structural disparities. We evaluate our hybrid model in an extensive case study predicting cherry tree phenology in Japan, South Korea and Switzerland. Our approach consistently outperforms both traditional biophysical and machine learning models in predicting blooming dates across years. Additionally, the neural network's adaptability facilitates parameter learning for specific tree varieties, enabling robust generalization to new sites without site-specific recalibration. This hybrid model leverages both biophysical constraints and data-driven flexibility, offering a promising avenue for accurate and interpretable phenology modeling.
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