精准灌溉中土壤湿度估计的性能触发自适应模型降阶
Performance triggered adaptive model reduction for soil moisture estimation in precision irrigation
AI总结：
针对大规模农田土壤湿度估计中农业水文模型高维难题，提出性能触发的自适应模型降阶方法，结合无监督机器学习与自适应EKF，在保证预测误差低于阈值的同时实现高效估计。
摘要：
准确的土壤湿度信息对于制定精准灌溉控制策略以提高水分利用效率至关重要。在处理大规模农田时，基于有限土壤湿度传感器的土壤湿度估计对于获取全面的土壤湿度信息十分关键。土壤湿度估计的主要挑战在于空间离散化农业水文模型的高维性。在本工作中，我们提出了一种性能触发的自适应模型降阶方法来应对这一挑战。所提出的方法采用基于轨迹的无监督机器学习技术，并设计了一种基于预测性能的触发方案，以自适应地控制模型更新，从而使降阶模型与原始模型在预测时域内的预测误差保持在预定阈值以下。基于降阶模型设计了一种自适应扩展卡尔曼滤波器（EKF）用于土壤湿度估计。通过将所提出的方法应用于模拟的大规模农田，对其适用性和性能进行了广泛评估。
英文摘要：
Accurate soil moisture information is crucial for developing precise irrigation control strategies to enhance water use efficiency. Soil moisture estimation based on limited soil moisture sensors is crucial for obtaining comprehensive soil moisture information when dealing with large-scale agricultural fields. The major challenge in soil moisture estimation lies in the high dimensionality of the spatially discretized agro-hydrological models. In this work, we propose a performance-triggered adaptive model reduction approach to address this challenge. The proposed approach employs a trajectory-based unsupervised machine learning technique, and a prediction performance-based triggering scheme is designed to govern model updates adaptively in a way such that the prediction error between the reduced model and the original model over a prediction horizon is maintained below a predetermined threshold. An adaptive extended Kalman filter (EKF) is designed based on the reduced model for soil moisture estimation. The applicability and performance of the proposed approach are evaluated extensively through the application to a simulated large-scale agricultural field.
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