【目的】红松(Pinus koraiensis)针叶油脂含量较高,存在极高的森林火灾风险,地表火蔓延是其主要的火灾传播方式。本研究通过构建地表火蔓延速率预测模型,为红松人工林的火灾防控提供科学依据。【方法】以黑龙江省凉水地区红松人工林枯落针叶层为材料,进行松针含水率为0、5%、10%、15%、20%,坡度为0、5°、10°、15°,风速为0、1、2、3、4、5 m/s的360组室内点烧试验,根据热电偶法测定火蔓延速率,构建改进鲸鱼优化算法(IWOA)-BP神经网络模型对火蔓延速率进行预测,并与3种模型(WOA-BP神经网络、GA-BP神经网络和PSO-BP神经网络)进行预测结果对比。【结果】坡度、风速与火蔓延速度均呈极显著正相关(P<0.01),含水率与火蔓延速度呈显著负相关(P<0.05);火蔓延速率随可燃物含水率的增加而降低,随风速和坡度的增加而升高,在风速为4 m/s时,火蔓延增长速率达到最大值。IWOA算法引入Tent混沌映射、改进非线性收敛因子、增加自适应权重和Levy飞行运动,增加了算法的随机性和多样性,提高了收敛速度,同时避免陷入局部最优,具备较高预测精度和鲁棒性;IWOA优化的BP神经网络模型精度和稳定性明显高于其他3种模型,对实测数据的模型适应度最佳。【结论】IWOA-BP神经网络模型能有效地预测红松人工林枯落针叶层的火蔓延速率,为林火防控与森林地表凋落物的火蔓延速率预测模型研究提供科学指导。

【Objective】Pinus koraiensis needles exhibit a significant forest fire risk due to their high oil content, and surface fire spread is the main fire spread mode. Developing a predictive model for surface fire spread rates can provide scientific basis and valuable insights for fire prevention and control in Pinus koraiensis plantations.【Method】The dead coniferous layer of Pinus koraiensis plantation in Liangshui area of Heilongjiang province was used as the material, 360 sets of indoor point burning tests were conducted with water content of 0, 5%, 10%, 15%, 20%, slope of 0°, 5°, 10°, 15° and wind speed of 0, 1, 2, 3, 4 and 5 m/s. Based on the fire spread rate measured by thermocouple method, an improved WOA(IWOA)-BP neural network model was constructed to predict the fire spread rate, and the prediction results were compared with those of three models (WOA-BP neural network, GA-BP neural network and PSO-BP neural network).【Result】Slope, wind speed and fire spread rate were significantly positively correlated (P<0.01), while water content exhibited a negative correlation with fire spread rate (P<0.05). The fire spread rate decreased with an increase in fuel water content, and increased with the increase of wind speed and slope. When the wind speed was 4 m/s, the fire spread growth rate reached the maximum. The improved whale optimization algorithm (IWOA) included Tent chaotic mapping, improved nonlinear convergence factor, adaptive weighting and Levy flight motion. These enhancements increased the algorithm’s randomness and diversity, thereby improving its convergence speed and reducing the likelihood of becoming trapped in local optima, with high prediction accuracy and robustness. The accuracy and stability of the BP neural network model optimized by the IWOA demonstrated significant improvements compared to three other models, exhibiting the highest model fitness to the measured data.【Conclusion】The IWOA-BP neural network model can effectively predict the fire spread rate of the dead coniferous layer of the Pinus koraiensis plantation, and providing scientific guidance for forest fire prevention and control and forest litter fire spread rate prediction model.