用机器学习从土壤水分数据反推土壤属性，精度高且抗干扰强。
Estimating properties of a homogeneous bounded soil using machine learning models
- 基于渗流方程模拟数据，将参数识别建模为双输出回归任务。
- 在全量、噪声、有限数据下，扩散率预测准确率高于水力传导率。
- 支持向量机与神经网络表现最优，鲁棒性强适合实际应用。
本文研究从土壤水分测量数据中估计土壤属性。采用福卡斯方法求解均质有限土层垂直入渗的初边值问题生成模拟数据。针对参数识别问题，将其建模为双输出回归任务，评估多种机器学习模型在不同数据条件（完整、含噪、有限）下的性能。总体而言，扩散率 $D$ 的预测精度高于水力传导率 $K$。在所考虑的模型中，支持向量机（SVMs）和神经网络（NNs）展现出最高鲁棒性，达到近乎完美的准确率且误差最小。
原文摘要 · Abstract (English)
This work focuses on estimating soil properties from water moisture measurements. We consider simulated data generated by solving the initial-boundary value problem governing vertical infiltration in a homogeneous, bounded soil profile, with the usage of the Fokas method. To address the parameter identification problem, which is formulated as a two-output regression task, we explore various machine learning models. The performance of each model is assessed under different data conditions: full, noisy, and limited. Overall, the prediction of diffusivity $D$ tends to be more accurate than that of hydraulic conductivity $K.$ Among the models considered, Support Vector Machines (SVMs) and Neural Networks (NNs) demonstrate the highest robustness, achieving near-perfect accuracy and minimal errors.
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