Multi-Region Temperature Prediction in Grain Storage: Integrating WSLP Spatial Structure with LSTM–iTransformer Hybrid Framework

Abstract

Grain security is a fundamental guarantee for social stability and sustainable development. Accurate monitoring and prediction of overall granary temperature are essential for reducing storage losses and improving warehouse management efficiency. As an integrated system, the temperature evolution of the grain pile is deeply affected by its inherent physical structure and heat transfer pathways. Therefore, a multi-level warehouse–surface–line–point (WSLP) structural modeling approach driven by the physical properties of the grain pile is proposed to extract the joint environmental and spatial characteristics. Building upon the WSLP framework, a dual-channel time-series prediction architecture integrating both long short-term memory (LSTM) and iTransformer through a mutual verification fusion mechanism is developed to enable synchronized temperature forecasting across different regions of the grain piles. Experiments are conducted using real granary data from Shandong, China. The results demonstrate that the proposed model achieves more than 30% improvement over baseline methods in terms of MAE and RMSE. Moreover, the WSLP-LSTM–iTransformer framework significantly improves prediction accuracy in complex warehouse environments and enhances the interpretability and applicability of deep learning models for grain condition forecasting by incorporating real environmental characteristics.