Time series classification, augmentation and artificial-intelligence-enabled software for emergency response in freight transportation fires

Abstract

In responding to freight transportation fire incidents, first responders refer to the terials labeled on the freights and the Emergency Response Guidebook (ERG) for guidance on the initial response. However, when the burning goods are mixed or unknown, first responders require support on the appropriate response strategy. In this work, we propose an artificial intelligence (AI) enabled tool to aid first responders in the initial emergency response to fire incidents involving mixed or unknown materials. We used many different machine learning models, including MultiRocket, Omni-Scale CNN (OS-CNN), Ultra-Fast Shapelets (UFA), WEASEL+MUSE, Dynamic Time Warping (DTW), Time Series Forest (TSF), the Random Interval Spectral Ensemble (RISE), and InceptionTime, to develop a trained machine learning (ML) model integrated into the AI tool. The ML model identifies the hazard characteristics (e.g., toxicity and explosivity) of a given fire, based on chemicals found in the effluent. The effluent data was collected from samples of burning materials using a cone calorimeter and a Fourier Transform Infrared Spectroscopy (FTIR) gas analyzer. We discuss the methodologies behind developing the ML model and demonstrate its high classification accuracy with time series augmentations to enhance the training dataset. We also implemented Self-Supervised Learning, Domain-Agnostic Contrastive Learning (DACL), and Adversarial Perturbation-based Latent Construction (APLR) to further improve the model. The results show that MultiRocket outperforms other models in terms of accuracy and running time. Furthermore, we developed a user interface for our AI-enabled tool, which uses a data sample as input to provide not only predictions of the fire hazards but also actionable intelligence to the first responders for safe and effective fire suppression strategies. Our proposed toolkit benefiting from AI will pave the way to further research in accessible, easily-deployable AI-enabled tools in fire sciences and management.Environmental Implications We leverage machine learning to predict the hazard classification of materials that may not be considered hazardous by Emergency Response Guidebook (ERG) used by Transport Canada, but may release toxic substances when burning. For example, polyvinyl chloride is not considered as hazardous by ERG, but toxic and corrosive hydrochloric gas is produced when burning. We aim to provide more decision support to first responders in freight transportation fires involving non-hazardous or unknown materials to identify the scope of the emergency from the early stage of the fire, which is critical to protect the environment from the effects of the fire.