基于改进邻域粗糙集和优化BPNN的火灾预测算法OA北大核心CSTPCD

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.