Quantifying Field Soil Moisture, Temperature, and Heat Flux Using an Informer–LSTM Deep Learning Model

Understanding water and heat transport through soils is vital for managing soil and groundwater resources, agricultural irrigation, and ecosystem protection. This paper aims to explore the potential application of deep learning methods in simulating water and heat transport processes within soils. It also examines the interactions between soil hydrological processes and environmental factors, including meteorological conditions and groundwater levels. To achieve these, we develop a hybrid model Informer–LSTM by combining two powerful architectures: Informer, a Transformer-based model essentially designed for long-sequence time-series forecasting, and Long Short-Term Memory (LSTM), a neural network that is great at learning short-term patterns in sequential data. The model is applied to field measurements from Henan Township in Ordos, Inner Mongolia, China, for training and testing, to simulate three key variables: soil water content, temperature, and heat flux at different depths in two soil columns with different groundwater levels. Our results confirm that Informer–LSTM is highly effective at simulating the soil water and heat transport. Simultaneously, we evaluate its performance by incorporating various combinations of input data including meteorological data, soil hydrothermal dynamics, and groundwater level. This reveals the relationship between soil hydrothermal processes and meteorological data, as well as coupled processes of soil water and heat transport. Moreover, employing SHapley Additive exPlanations (SHAP) analysis, we identify the most influential factors for predicting heat flux in shallow soils. This research demonstrates that deep learning models are a viable and valuable tool for simulating soil hydrothermal processes in arid and semi-arid regions.