A data-driven model for tunnel fire scenario deduction based on optimized informer

Abstract The prediction models for tunnel fires require exceptionally high accuracy and responsiveness due to the complex enclosed environment and associated high-risk characteristics. However, existing temporal prediction models demonstrate limitations in processing multidimensional data and managing long-term dependencies, making them inadequate for effectively addressing the prediction requirements of tunnel fires. This study introduces the bidirectional down-sampling informer (BiDS-Informer) model, which is built upon innovative components, including the BiDS module, dynamic attention mechanisms, and encoder dimensionality reduction optimization. (1) Within the encoder, the BiDS-Informer utilizes the bidirectional down-sampling module for feature extraction, thereby obtaining a more comprehensive representation of features; (2) the traditional self-attention mechanism is restructured into dynamic attention mechanisms, allowing the model to adaptively identify and process the characteristics of multidimensional time series data; (3) finally, dimensionality reduction is applied to the encoder’s input structure to eliminate temporal encoding information, resulting in enhanced computational efficiency. Experimental results indicate that the proposed model outperforms existing time series prediction models regarding predictive accuracy across multiple real-world datasets, particularly on the tunnel fire dataset. Compared to the informer model, the proposed model shows improvements of 18.56% and 27.49% in average MSE and MAE, respectively, on the tunnel fire dataset. This model provides substantial technical support for enhancing the reliability of tunnel fire early warning systems and holds promising potential for widespread application.