Machine learning based prediction method for the heat release rate of a fire source

An accurate measurement of the heat release rate (HRR) of a fire source is crucial for thoroughly understanding the fire evolution process. However, the commonly used oxygen consumption method requires expensive equipment, leading to high operational costs. According to the energy conservation principle, heat released per unit of time during material combustion is closely related to the increase of the room temperature. Machine learning methods have demonstrated considerable potential for exploring relationships between several independent and dependent variables, and attempts have been made to predict fire parameters based on temperature. Therefore, based on previous studies, this paper proposes a comprehensive machine learning framework to predict the HRR using temperature data as input. Furthermore, feature selection techniques are innovatively introduced to obtain key location temperatures to maximize HRR prediction accuracy.

First, fire scenarios with different parameters are simulated in an ISO 9705 room using the fire dynamics simulator (FDS) software. Temperature data at different locations are obtained by gridding thermocouples, and a fire database is constructed. Subsequently, feature selection using recursive feature elimination (RFE) algorithms based on least absolute shrinkage and selection operator (Lasso) and random forest (RF) is performed to obtain two different low-dimensional subsets from high-dimensional simulated temperature features. Control groups with the same number of features are established. Finally, the performance of three typical models, namely, linear regression (LR), K-nearest neighbors (KNN), and light gradient boosting machine (LightGBM), for predicting the HRR are compared using different feature subsets.

The results show that using the subset obtained by RFE based onRF, the LightGBM model demonstrates the lowest root mean square error (RMSE) and mean absolute error (MAE) values of 23.89 kW and 15.49 kW, respectively, indicating the least difference between its predicted and observed values. Furthermore, regarding the coefficient of determination, the LightGBM model reaches the highest value of 0.991 6, close to 1, thereby demonstrating its superior fitting capability. This is primarily due to the complex nonlinear relationship between the trained temperature dataset and HRR. Compared to the KNN and LR models, the histogram algorithm and the leaf-wise growth strategy with a depth limit enable the LightGBM model to give full play to its advantages. Tree-based models, such as LightGBM and XGBoost, can also be used for algorithm model-level improvements in the future. Additionally, deep learning models, such as multilayer fully connected and convolutional neural networks, can be utilized for fitting complex mappings. Compared with LightGBM models trained with manual feature subsets, RFE based on RF decreases the prediction errors (RMSE and MAE values decrease by 46.54% and 50.66%, respectively). The coefficient of determination also increases by 2.1%, validating that this feature selection method can considerably improve prediction accuracy over manual feature selection.

This paper proposes a comprehensive machine learning framework to obtain thermocouple temperature through the FDS fire simulation and then combines the feature selection and prediction models for HRR prediction, substantially improving prediction accuracy over manual feature selection. This comprehensive framework has theoretical and practical significance, thereby opening new pathes for HRR prediction. Subsequent research studies will construct additional comprehensive fire databases, increase the number of feature parameters, or explore algorithm combinations to improve HRR prediction.