基于PSO-SVM-SST模型的地震应急物资需求预测研究OA

A post-earthquake affected population prediction model based on support vector machines(SVM)optimized by particle swarm optimization(PSO)is established,and the SST(safety stock theory)earthquake emergency supply demand prediction model is constructed.Nine indicator parame-ters,including seismic hazard and damage severity,are selected and processed through dimensionality reduction and redundancy removal as input variables for the PSO-optimized SVM model to predict the affected population.Based on the relationship between the affected population and emergency supplies in disaster areas,the SST model is applied to indirectly estimate the quantities of typical supplies re-quired in the immediate aftermath of the Jiuzhaigou earthquake.The experimental results are as fol-lows:By employing an error comparison analysis method to validate the model's effectiveness,the PSO-SVM model demonstrates a 14.27％reduction in prediction error compared to the SVM model,with a significant improvement in prediction accuracy.The estimated demand for typical supplies in the aftermath of the Jiuzhaigou earthquake provides a certain degree of reference,indicating that the PSO-SVM-SST prediction model possesses both theoretical and practical rationality and utility.