一种面向智能灌溉决策支持的不确定性感知混合数学-机器学习模型
An Uncertainty-Aware Hybrid Mathematical-Machine-Learning Model for Smart Irrigation Decision Support
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中文总结 AI 辅助
本研究提出一种耦合水量平衡与随机森林的混合模型，通过共形预测量化不确定性并驱动风险感知灌溉决策，在24小时预测时域显著优于基线，证明不确定性量化是预测驱动灌溉可信度的关键。
中文摘要 AI 辅助
农业约占全球淡水取水量的70%，然而灌溉通常仍采用反应式调度，既没有对土壤湿度变化趋势的预测，也没有对该预测置信度的说明。数据驱动模型准确但不透明且仅提供点值；水量平衡模型透明但存在较大的结构性误差。两者单独使用均无法在不确定性下支持合理的灌溉决策。本研究将两者耦合，并将不确定性传递至决策环节：一个仅基于训练数据校准的四参数水量平衡核心，由随机森林进行修正，该随机森林仅学习物理残差；共形预测附加90%标称区间；一个风险感知规则将区间下界转化为灌溉触发条件。该模型在严格时间顺序划分下，基于一个雨养地中海农田站点三年的逐小时原位测量数据进行了评估，并与从一小时到一周的持续性预测进行了对比。在24小时预测时域，混合模型达到均方根误差0.00925立方米每立方米，技能提升9.4%，约为九个基线中最佳模型的两倍，其中仅随机森林优于持续性预测。技能并未随预测时域增长而增加：在三小时时达到峰值+27.4%，在一周时降至+1.2%。共形分位数回归的校准更好，且比恒定宽度共形预测窄11%。风险感知规则将提前检测到的管理阈值越界比例从0.905提升至1.000，代价是精确率从0.975降至0.950，名义用水量增加3.7%；超过72小时后，点预测低于无预测规则，而基于区间的规则则不然。因此，不确定性量化决定了基于预测的灌溉建议保持可信的预测时域，且此处物理层的透明性未以可测量的准确性为代价。
英文摘要
Agriculture accounts for roughly 70% of global freshwater withdrawals, yet irrigation is still commonly scheduled reactively, with no forecast of where soil moisture is heading and no statement of confidence in that forecast. Data-driven models are accurate but opaque and point-valued; water-balance models are transparent but carry large structural error. Neither alone supports a defensible irrigation decision under uncertainty. This study coupled the two and carried uncertainty through to the decision: a four-parameter water-balance core, calibrated on training data only, was corrected by a Random Forest that learned nothing but the physical residual, conformal prediction attached 90%-nominal intervals, and a risk-aware rule converted the interval lower bound into an irrigation trigger. It was evaluated on three years of hourly in-situ measurements from a rainfed Mediterranean cropland station under a strict chronological split, scored against persistence, from one hour to one week. At the 24 h horizon the hybrid reached RMSE 0.00925 m^3 m^-3 and +9.4% skill, roughly double the best of nine baselines, of which only the Random Forest beat persistence. Skill did not grow with lead time: it peaked at +27.4% at three hours and fell to +1.2% at one week. Conformalised quantile regression was better calibrated and 11% sharper than constant-width conformal prediction. The risk-aware rule raised management-threshold crossings detected in advance from 0.905 to 1.000, at a precision cost of 0.975 to 0.950 and 3.7% more notional water, and beyond 72 h the point forecast fell below the no-forecast rule while the interval-based rule did not. Uncertainty quantification therefore governs the lead time over which forecast-driven irrigation advice remains trustworthy, and here transparency in the physical layer cost no measurable accuracy.
