A decision making model for chemical accident responses based on machine learning

Abstract

Chemical accidents are one of the biggest obstacles to the development of the chemical industry. Prevention and mitigation should be considered to reduce the damage of chemical accident ahead. When they are failed emergency response should play an important role. An emergency evacuation order is one of the emergency response method to mitigate the damage after the accident occurred. In order for a chemical emergency response method such as emergency evacuation order to be effective, its accuracy and quickness must be secured. In the previous study, a model of predicting issuing emergency evacuation order using machine learning was created. The previous prediction model studied 61,563 chemical accident data in NTSIP(National Toxic Substance Incidents Program) from 1996 to 2009 in the United States, and the learning objective was the issuance of emergency evacuation orders under certain accident situation. However, incorrect evacuation order could have been issued in its history and the methodology needed to be improved. In this study, the necessity of emergency evacuation orders was determined by using logical judgment and machine learning in order to solve the problems using the same database. For logical judgment, information on issuing emergency evacuation orders and civilian victim information in the chemical accident database were used. The accuracy of the model was increased and it was confirmed that it could be used significantly in the chemical accident responses.