Soil and Tillage Research最新文献

英文中文

Simulating field soil temperature variations with physics-informed neural networks 利用物理信息神经网络模拟田间土壤温度变化

Soil and Tillage Research

Pub Date : 2024-07-15 DOI: 10.1016/j.still.2024.106236

Xiaoting Xie, Hengnian Yan, Yili Lu, Lingzao Zeng

Information on soil temperature is crucial for modeling hydrological and climatic processes. Nevertheless, direct measurements of soil temperature are usually rather limited in space, leading to an urgent need for improved spatial resolution. To address this issue, a Physics-Informed Neural Networks (PINN) method for estimating soil temperature () profile variations was proposed in this study. This method combines the advantages of Deep Neural Networks (DNN) in modeling complex non-linear relationships and physical laws for more robust predictions. The performance was evaluated using in-situ annual soil at depths of 5 cm, 10 cm and 20 cm on a maize field in Northeast China. Cross-validation was used, a PINN was used to derive the new data at unobserved depth from observations at the other two depths. The results demonstrated that the performance of the PINN was superior to the commonly used process-based method and a DNN for all situations. Compared to the traditional method, the PINN achieved a 0.69°C and 0.39°C reduction in root-mean-square error (RMSE) for estimates at 10 cm and 20 cm depths, respectively, under plowed tillage condition, while it could also accurately estimate at 5 cm depth with RMSE of 0.56 °C. In addition, the PINN does not require inputs of soil thermal properties e.g., apparent thermal diffusivity (κ), as the space and time-dependent κ values could also be learned during the training process. The results presented here demonstrated that a PINN could successfully utilize limited observation data to estimate unknown soil profiles, and solve some challenging problems beyond the reach of existing methods in simulating soil thermal dynamics.

土壤温度信息对于水文和气候过程建模至关重要。然而，土壤温度的直接测量通常空间有限，因此迫切需要提高空间分辨率。为解决这一问题，本研究提出了一种用于估算土壤温度（）剖面变化的物理信息神经网络（PINN）方法。该方法结合了深度神经网络（DNN）在模拟复杂的非线性关系和物理规律方面的优势，可实现更稳健的预测。该方法使用中国东北地区玉米田中 5 厘米、10 厘米和 20 厘米深的原位年度土壤进行了性能评估。采用了交叉验证方法，利用 PINN 从其他两个深度的观测数据推导出未观测深度的新数据。结果表明，在所有情况下，PINN 的性能都优于常用的基于过程的方法和 DNN。与传统方法相比，在耕作条件下，PINN 在 10 厘米和 20 厘米深度的估计值均方根误差（RMSE）分别减少了 0.69°C 和 0.39°C，在 5 厘米深度也能准确估计，RMSE 为 0.56°C。此外，PINN 不需要输入土壤热属性（如表观热扩散率 (κ)），因为与空间和时间相关的 κ 值也可以在训练过程中学习到。本文介绍的结果表明，PINN 可以成功地利用有限的观测数据来估计未知的土壤剖面，并解决在模拟土壤热动力学方面现有方法无法解决的一些挑战性问题。

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