A comprehensive study of deep learning for soil moisture prediction

Abstract

Abstract. Soil moisture plays a crucial role in the hydrological cycle, but accurately predicting soil moisture presents challenges due to the nonlinearity of soil water transport and variability of boundary conditions. Deep learning has emerged as a promising approach for simulating soil moisture dynamics. In this study, we explore ten different network structures to uncover their mechanisms of data utilization and maximize the potential of deep learning for soil moisture prediction, including three basic feature extractors and seven diverse hybrid structures, six of which are applied to soil moisture prediction for the first time. We compare the predictive abilities and computational costs of the models across different soil textures and depths systematically. Furthermore, we exploit the interpretability of the models to gain insights into their workings and attempt to advance our understanding of deep learning in soil moisture dynamics. For soil moisture forecasting, our results demonstrate that the temporal modeling capability of Long Short-Term Memory (LSTM) is well-suited. Besides, the improved accuracy achieved by feature attention LSTM (FA-LSTM) and the generative adversarial network-based LSTM (GAN-LSTM), along with the Shapley additive explanations (SHAP) analysis, help us discover the effectiveness of attention mechanisms and the benefits of adversarial training in feature extraction. These findings provide effective network design principles. The Shapley values also reveal varying data leveraging approaches among different models. The t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization illustrates differences in encoded features across models. In summary, our comprehensive study provides insights into soil moisture prediction and highlights the importance of the appropriate model design for specific soil moisture prediction tasks. We also hope this work serves as a reference for deep learning studies in other hydrology problems. The codes of 3 machine learning and 10 deep learning models are open sourced.