Modeling Thunderstorm Based on Paralleled and Improved Naive Bayes

Abstract

Aiming at improving the low efficiency and inaccuracy of the traditional thunderstorm prediction model, a paralleled and improved naive Bayesian thunderstorm prediction model SPNBC is proposed. The native Bayesian classifier (NBC) model assumes that the attribute data set is independent, which leads to inaccurate prediction. The principal component analysis algorithm is used to optimize the Bayesian algorithm to build PNBC. First, a new attribute data set is constructed by the principal component analysis (PCA) algorithm to eliminate the dependency between attributes, and a naive Bayesian classifier is constructed on the new attribute data set. Secondly, SPNBC is obtained by parallel design of PNBC based on spark framework to improve the time efficiency. Finally, taking the thunderstorm prediction in Hohhot as the application background, the accuracy rate, false alarm rate, speedup ratio and scalability ratio were used to analyze the experiment. The experimental results show that the SPNBC of this paper is better than the traditional naive Bayes algorithm and neural network algorithm, and the prediction accuracy rate and empty alarm rate are larger when dealing with massive data Performance advantages.