Machine learning based quantitative consequence prediction models for toxic dispersion casualty

Incidental release of toxic chemicals can pose extreme danger to life in the vicinity. Therefore, it is crucial for emergency responders, plant operators, and safety professionals to have a fast and accurate prediction to evaluate possible toxic dispersion life-threatening consequences. In this work, a toxic chemical dispersion casualty database that contains 450 leak scenarios of 18 toxic chemicals is constructed to develop a machine learning based quantitative property-consequence relationship (QPCR) model to estimate the affected area caused by toxic chemical release within a certain death rate. The results show that the developed QPCR model can predict the toxic dispersion casualty range with root mean square error of maximum distance, minimum distance, and maximum width less than 0.2, 0.4, and 0.3, which indicates that the constructed model has satisfying accuracy in predicting toxic dispersion ranges under different lethal consequences. The model can be further expanded to accommodate more toxic chemicals and leaking scenarios.

Automated summary

In order to assess the potential life-threatening consequences of toxic dispersion, it is crucial for emergency responders, plant operators, and safety professionals to have a fast and accurate prediction. In this study, a toxic chemical dispersion casualty database containing 450 leak scenarios of 18 toxic chemicals is constructed to develop a machine learning based quantitative property-consequence relationship (QPCR) model. The results show that the developed QPCR model can accurately predict the toxic dispersion casualty range under different lethal consequences.