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Interpretable soil moisture prediction with a knowledge-guided deep learning approach
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DOI：10.5194/hess-30-2973-2026.png)
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Abstract
En 中文
Abstract. Soil moisture (SM) is a critical component of the hydrological cycle; but accurately predicting it remains challenging due to the nonlinearity of soil water transport; variability in boundary conditions; and the intricate nature of soil properties. Recently; deep learning has shown promise in this domain; typically by modeling temporal dependencies for soil moisture predictions. In this study; we propose non-local neural networks (NLNNs) to convert this problem into a single-time-step; simultaneous multi-depth soil moisture forecasting. The non-local operation design includes embedded Gaussian operations and disentangled knowledge-guided operations; resulting in two variants: the self-attention non-local neural network (SA-NLNN) and the knowledge-guided non-local neural network (KG-NLNN). The knowledge-guided non-local operation is designed to capture vertical soil moisture relationships by decomposing the influences on soil moisture at a given depth into four components; each governed by distinct physical processes. The models offer visual interpretability through learned non-local weights; which reveal interactions among soil moisture across different depths; thereby enabling a qualitative representation of inter-layer connectivity. Notably; the model guided by soil moisture transport knowledge yields more stable and reasonable interpretations. With in-situ observations; we demonstrate that our proposed models perform satisfactorily. The knowledge-guided non-local operations significantly enhance accuracy and reliability. Additionally; our models adapt to diverse time-scale situations while maintaining high computational efficiency. Both models exhibit robust noise resistance; with knowledge guidance enhancing KG-NLNN's noise resistance. In summary; our work addresses the soil moisture prediction challenge in a novel way; highlighting the potential of NLNN and the importance of incorporating physic guidance in data-driven models.
Keywords:
soil moisture prediction
deep learning
non-local neural networks
knowledge-guided models
interpretability
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