首页|基于改进邻域粗糙集和优化BPNN的火灾预测算法

基于改进邻域粗糙集和优化BPNN的火灾预测算法

Fire prediction algorithm based on improved neighborhood rough set and optimized BPNN

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
针对传统森林火灾检测算法精度低,以及大规模、多特征的火灾数据存在冗余信息等问题,该文提出了一种基于改进邻域粗糙集的优化反向传播神经网络(BPNN)火灾预测方法.首先,考虑到数据集具有高维特征空间和高度特征冗余等特点,设计出一种基于混沌反学习蝙蝠(BA)算法的邻域粗糙集特征选择算法,对火灾原始数据集进行特征寻优,得到约简属性子集;然后,构建BA算法优化的BPNN预测模型,将约简属性子集输入该模型中,得到火灾预测的结果;最后,通过平均分类准确度、F1值、精确度、曲线面积、召回率、平均误差率这6种评价指标,在UCI公开森林火灾数据集上分析和检验模型的分类性能.在2个数据集上的实验结果显示,基于混沌反学习策略的算法准确率为94.3％和52.7％,与邻域粗糙集结合后准确率达到98.1％和59.6％,证明了该文算法具备较高的检测精度.
In response to the low accuracy of traditional forest fire detection algorithms and the redundancy in large-scale,multi-feature fire data,this paper proposes a fire prediction method based on an optimized back propagation neural network(BPNN)with an improved neighborhood rough set.Firstly,considering the characteristics of the dataset such as high-dimensional feature space and high feature redundancy,a neighborhood rough set feature selection algorithm based on the chaotic anti-learning bat algorithm(BA)is designed to optimize the features of the original fire dataset,obtaining a reduced attribute subset.Then,a BPNN prediction model optimized by the BA is constructed,into which the reduced attribute subset is fed to obtain fire prediction results.Finally,the classification performance of the model is analyzed and tested on the UCI public forest fire dataset through six evaluation metrics:average classification accuracy,F1 score,precision,area under the curve,recall,and average error rate.Experimental results on 2 datasets show that the accuracy of the algorithm based on the chaotic anti-learning strategy is 94.3％and 52.7％,and after combining with the neighborhood rough set,the accuracy reaches 98.1％and 59.6％,proving that the proposed algorithm possesses high detection accuracy.

back propagation neural networkneighborhood rough setbat algorithmopposition-based learningchaotic mappingforest firemachine learningpredictive model

许诗卉、徐久成、瞿康林、杨杰、周长顺

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河南师范大学计算机与信息工程学院,河南新乡 453007

智慧商务与物联网技术河南省工程实验室,河南新乡 453007

反向传播神经网络 邻域粗糙集 蝙蝠算法 反向学习 混沌映射 森林火灾 机器学习 预测模型

国家自然科学基金国家自然科学基金国家自然科学基金

619760826207608962002103

2024

10.14177/j.cnki.32-1397n.2024.48.02.009

南京理工大学学报(自然科学版)
南京理工大学

南京理工大学学报(自然科学版)

CSTPCD北大核心
影响因子：0.526
ISSN：1005-9830
年,卷(期)：2024.48(2)
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