Pool fires pose a threat to the safety and environmental protection of industrial production. The burning rate is one of the most important burning parameters that determines the behavior of pool fires. Back Propagation Neural Network (BPNN) and Extreme Gradient Boosting Tree (XGBoost) were used to predict the burning rate of n-heptane pool fire with the effect of fuel depth, ullage depth, area and perimeter of pool pans, atmospheric pressure and speed of cross air flow in open spaces. The results show that after optimizing the model parameters using RandomizedSearchCV, both models yielded higher precision evaluation results. Compared with BPNN, the proposed XGBoost model demonstrates better prediction performance, achieving a R of 0.9744, RMSE of 4.1068, and MAE of 2.5036 in test set. The impact of different hyperparameters and data normalization on model prediction accuracy was explored. The impact of fuel depth, ullage depth, atmospheric pressure, speed of cross air flow, area, and perimeter on burning rate was determined using SHapley Additive exPlanations (SHAP) sensitivity analysis. This analysis aids in conducting risk analysis for pool fires, and offers early warning and feasible reference for fire safety hazards.

中文翻译：

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基于BPNN和XGBoost的露天正庚烷池火燃烧速率预测与比较


泳池火灾对工业生产的安全和环境保护构成威胁。燃烧速率是决定池火行为的最重要的燃烧参数之一。利用反向传播神经网络（BPNN）和极限梯度提升树（XGBoost）预测正庚烷池火灾的燃烧速率，并考虑燃料深度、缺损深度、池盘面积和周长、大气压力和速度的影响。开放空间中的交叉气流。结果表明，使用RandomizedSearchCV优化模型参数后，两个模型都获得了更高精度的评估结果。与BPNN相比，所提出的XGBoost模型表现出更好的预测性能，在测试集中实现了R为0.9744，RMSE为4.1068，MAE为2.5036。探讨了不同超参数和数据归一化对模型预测精度的影响。使用 SHapley Additive exPlanations (SHAP) 灵敏度分析确定燃料深度、空缺深度、大气压力、横向气流速度、面积和周长对燃烧速率的影响。该分析有助于对泳池火灾进行风险分析，为消防安全隐患提供预警和可行参考。