Application of the improved K-SMOTE random forest algorithm to the safety risk assessment of seismic information release
The safe,timely,and effective dissemination of special information such as earthquake monitoring,early warning,disaster assessment,and emergency is crucial for maintaining social safety and stability.This paper proposed an improved K-means synthetic minority oversampling technique(K-SMOTE)combined with the random forest method to effectively assess the safety risks associated with the release of this information and address potential weak links.A seismic information safety risk assessment model was then established,using the improved K-SMOTE algorithm to generate a well-balanced sample set.Finally,the random K-fold cross-validation method is applied to divide the samples and optimize the model,enabling the assessment of risk levels for target safety.Using an actual earthquake information release case as an example,the model proposed in this paper achieves an evaluation accuracy of 92%.The precision and recall of the model are 0.81 and 0.92,respectively,showing its strong generalization capability.These results indicate that the model is highly effective for assessing the safety risks associated with earthquake information release.This research provides valuable insights for improving the seismic information safety assessment system,enhancing the environment for publishing seismic infor-mation,and reducing safety risks.
seismic information releaserisk level assessmentimproved K-SMOTErandom forestrandom K-fold cross validation