首页|基于多特征参数的GA-WOA-BP火灾概率预测模型研究

基于多特征参数的GA-WOA-BP火灾概率预测模型研究

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为进一步提升火灾概率预测的准确率,针对BP神经网络在拟合过程中探测精度低、泛化能力差的问题,提出一种基于多特征参数的GA-WOA-BP火灾概率预测模型.首先通过试验采集了榉木、棉绳阴燃、明燃时的火灾特征参量,计算后得到了相应的火灾类型发生概率;其次通过遗传算法优化BP神经网络的隐藏层结构,鲸鱼优化算法优化BP神经网络的初始权重,构建了GA-WOA-BP模型,提高融合算法的拟合能力.最后,以多特征火灾参数作为模型输入,以不同类型火灾发生概率作为输出完成火灾概率的预测.结果表明,相比单纯BP神经网络,基于多特征参数的GA-WOA-BP火灾概率预测模型具有更好的预测性能,其评价指标RMSE、MAE、R2 分别为 0.020 22、0.014 33和0.992 31,能为火灾概率预测提供数据参考.
Research on GA-WOA-BP fire probability prediction model based on multi-feature parameters
In order to further improve the accuracy of fire probabil-ity prediction,a GA-WOA-BP fire probability prediction model based on multi-feature parameters is proposed to solve the prob-lems of low detection accuracy and poor generalization ability in the fitting process of BP neural network.Firstly,the fire charac-teristic parameters of beech and cotton rope smoldering and open burning were collected by experiment,and the corresponding probability of fire type was obtained after calculation.Secondly,by using genetic algorithm to optimize the hidden layer structure of BP neural network and whale optimization algorithm to opti-mize the initial weight of BP neural network,GA-WOA-BP model was constructed to improve the fitting ability of fusion algo-rithm.Finally,multi-characteristic fire parameters are used as model input and different types of fire occurrence probabilities are used as output to predict the fire probability.The results show that compared with the simple BP neural network,the model has better prediction performance,and its evaluation indexes RMSE,MAE and R2 are 0.020 22,0.014 33 and 0.992 31,respectively,which can provide data reference for fire probability prediction.

multi-feature parameterwhale optimization algo-rithmgenetic algorithmfire probability predictionBP neural net-work

刘全义、吴孟洋、艾洪舟、朱培

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中国民用航空飞行学院 民航安全与工程学院,四川 广汉 618307

中国民用航空飞行学院 民机火灾科学与安全工程四川省重点实验室,四川 广汉 618307

南京航空航天大学 民航学院,江苏 南京 210000

多特征参数 鲸鱼优化算法 遗传算法 火灾概率预测 BP神经网络

国家自然科学基金民机火灾科学与安全工程四川省重点实验室项目民机火灾科学与安全工程四川省重点实验室项目航空科学基金四川省院校合作项目民航应急科学与技术重点实验室项目民航应急科学与技术重点实验室项目

U2033206MZ2022JB01MZ2022KF08ASFC-202000461170012024YFHZ0027NJ2022022NJ2023025

2024

10.20168/j.1009-0029.2024.06.0820.06

消防科学与技术
中国消防协会

消防科学与技术

CSTPCD北大核心
影响因子：0.846
ISSN：1009-0029
年,卷(期)：2024.43(6)
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