Prediction of fire source heat release rate based on machine learning method

Accurate measurement of fire source heat release rate is crucial for comprehensively understanding the fire evolution process. However, the widely used oxygen consumption method requires expensive equipment, incurring high costs. This study proposes a comprehensive framework based on machine learning to predict fire source heat release rate using temperature as input. Firstly, fire scenarios with different parameters in ISO9705 room were simulated using FDS software to obtain temperature at various locations, establishing a fire database. Then, two recursive feature elimination algorithms based on the Lasso and the Random Forest (RF) models were employed separately for feature selection, resulting in two different low-dimensional feature subsets and a control group. Finally, different feature subsets were input to analyse and compare the prediction performance on the heat release rate of three typical algorithms: linear regression (LR), K-nearest neighbor (KNN), and lightGBM. Results indicate that the LightGBM model trained with the feature subset selected by the recursive feature elimination algorithm based on the Random Forest model exhibits the best predictive performance, with root mean square error (RMSE) and mean absolute error (MAE) of 23.89 kW and 15.49 kW respectively, and a coefficient of determination (R2) of 0.9916. This comprehensive framework based on machine learning demonstrates excellent predictive performance and is cost-effective, providing a new and effective approach for predicting fire source heat release rate.