Comparison of machine learning models for prediction of safety distances in toxic dispersion scenarios

Abstract

In the recent years, Machine Learning (ML) has demonstrated a significant potential in handling big amounts of diverse data and generating rapid and accurate predictions. These capabilities, applied in risk analysis studies, can be particularly valuable in emergency planning of industrial sites, providing timely and reasonably accurate information during and after accidents. This work focuses on the determination of safety distances, which are an indispensable part of the risk assessment of Seveso establishments. Specifically, ML models are implemented to predict safety distances in toxic dispersion scenarios of three substances, namely ammonia, chlorine and ethylene oxide. For the purpose of training the models, a database is constructed using ALOHA software, containing different scenarios over a wide range of atmospheric and process conditions that influence the release and the dispersion phenomena. Four distinct supervised ML models are deployed to perform classification and regression tasks, predicting distances for the intervention zone. The models are tuned and evaluated based on common statistical metrics, as well as a case-specific metric. The results indicate that classification is achieved in all the cases with similar degree of agreement, while regression varies significantly among the models.