A novel transformer-based approach for soil temperature prediction
This work addresses soil temperature forecasting, a critical parameter for glacier dynamics, hydrology, and agriculture, representing an incremental advance by applying transformers to this domain for the first time.
The paper tackled soil temperature prediction by introducing a novel transformer-based approach, achieving new state-of-the-art results as demonstrated through experiments with six FLUXNET stations and comparisons to deep learning and literature studies.
Soil temperature is one of the most significant parameters that plays a crucial role in glacier energy, dynamics of mass balance, processes of surface hydrological, coaction of glacier-atmosphere, nutrient cycling, ecological stability, the management of soil, water, and field crop. In this work, we introduce a novel approach using transformer models for the purpose of forecasting soil temperature prediction. To the best of our knowledge, the usage of transformer models in this work is the very first attempt to predict soil temperature. Experiments are carried out using six different FLUXNET stations by modeling them with five different transformer models, namely, Vanilla Transformer, Informer, Autoformer, Reformer, and ETSformer. To demonstrate the effectiveness of the proposed model, experiment results are compared with both deep learning approaches and literature studies. Experiment results show that the utilization of transformer models ensures a significant contribution to the literature, thence determining the new state-of-the-art.