Optimized Machine Learning Model for Fire Consequence Prediction

• Published in: Fire (Basel, Switzerland) Vol. 7; no. 4; p. 114
• Main Authors: Zhong, Wei, Wang, Shuangli, Wu, Tan, Gao, Xiaolei, Liang, Tianshui
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2024
• Subjects: Accuracy; Algorithms; Back propagation networks; Computer simulation; Construction accidents & safety; Data mining; Decision making; Deep learning; Distance; Explosions; Fire prevention; Fires; Forest & brush fires; Gasoline; Hazard assessment; Integrated approach; Learning algorithms; Machine learning; Model accuracy; Neural networks; Organic chemicals; PHAST; Poisoning; pool fire; prediction; Prediction models; Predictions; Radiant flux density; Regression analysis; Regression models; Risk assessment; Safety management; Simulation; Software; Storage tanks; Thermal radiation; Ventilation; China; United Kingdom > UK
• ISSN: 2571-6255; 2571-6255
• DOI: 10.3390/fire7040114

This article focuses on using machine learning to predict the distance at which a chemical storage tank fire reaches a specified thermal radiation intensity. DNV’s Process Hazard Analysis Software Tool (PHAST) is used to simulate different scenarios of tank leakage and to establish a database of tank accidents. Backpropagation (BP) neural networks, random forest models, and the optimized random forest model K-R are used for model training and consequence prediction. The regression performance of the models is evaluated using the mean squared error (MSE) and R2. The results indicate that the K-R regression prediction model outperforms the other two machine learning algorithms, accurately predicting the distance at which the thermal radiation intensity is reached after a tank fire. Compared with the simulation results, the model demonstrates higher accuracy in predicting the distance of tank fire consequences, proving the effectiveness of machine learning algorithms in predicting the range of consequences of tank storage area fire events.