MARLP：面向农业管理含水层补给的时间序列预测控制
MARLP: Time-series Forecasting Control for Agricultural Managed Aquifer Recharge
AI总结：
本文提出首个端到端数据驱动 Ag-MAR 控制系统 MARLP，通过两阶段土壤氧气预测和启发式 MPC 优化淹没调度，使氧气亏缺率降低 86.8%、单位时间补给量提高 35.8%。
摘要：
全球地下水迅速下降对可持续农业构成了重大挑战。为解决这一问题，人们提出了农业管理含水层补给（Ag-MAR），通过利用地表水人工淹没农田来补给含水层。Ag-MAR 需要精心选择淹没时间表，以避免影响作物根系的氧气吸收。然而，当前的 Ag-MAR 调度没有考虑天气和土壤氧气等复杂环境因素，导致作物受损且补给量不足。本文提出 MARLP，这是首个面向 Ag-MAR 的端到端数据驱动控制系统。我们首先将 Ag-MAR 形式化为一个优化问题。为此，我们分析了四年的田间数据集，揭示了土壤氧气水平趋势的多周期性特征，以及利用外部天气预报和淹没方案作为土壤氧气预测外生线索的机会。然后，我们设计了一个两阶段预测框架。在第一阶段，该框架从历史数据中提取跨变量依赖关系和周期性模式，以进行初步预测。在第二阶段，它利用天气-土壤和淹没-土壤因果关系来促进土壤氧气水平的准确预测。最后，我们对 Ag-MAR 淹没实施模型预测控制（MPC）。为应对大动作空间的挑战，我们设计了一个启发式规划模块，以减少淹没方案的数量，从而能够搜索最优解。真实世界实验表明，与此前四年相比，MARLP 将氧气亏缺率降低了 86.8%，同时将单位时间补给量提高了 35.8%。
英文摘要：
The rapid decline in groundwater around the world poses a significant challenge to sustainable agriculture. To address this issue, agricultural managed aquifer recharge (Ag-MAR) is proposed to recharge the aquifer by artificially flooding agricultural lands using surface water. Ag-MAR requires a carefully selected flooding schedule to avoid affecting the oxygen absorption of crop roots. However, current Ag-MAR scheduling does not take into account complex environmental factors such as weather and soil oxygen, resulting in crop damage and insufficient recharging amounts. This paper proposes MARLP, the first end-to-end data-driven control system for Ag-MAR. We first formulate Ag-MAR as an optimization problem. To that end, we analyze four-year in-field datasets, which reveal the multi-periodicity feature of the soil oxygen level trends and the opportunity to use external weather forecasts and flooding proposals as exogenous clues for soil oxygen prediction. Then, we design a two-stage forecasting framework. In the first stage, it extracts both the cross-variate dependency and the periodic patterns from historical data to conduct preliminary forecasting. In the second stage, it uses weather-soil and flooding-soil causality to facilitate an accurate prediction of soil oxygen levels. Finally, we conduct model predictive control (MPC) for Ag-MAR flooding. To address the challenge of large action spaces, we devise a heuristic planning module to reduce the number of flooding proposals to enable the search for optimal solutions. Real-world experiments show that MARLP reduces the oxygen deficit ratio by 86.8% while improving the recharging amount in unit time by 35.8%, compared with the previous four years.
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。
