基于PSO-SVM的鹤盛溪流域山洪风险评价OA北大核心CSTPCDEI

To explore the spatial distribution of torrential flood risk in the Heshengxi Watershed of Wenzhou City,a risk assessment model of torrential flood based on particle swarm optimization-support vector machine(PSO-SVM)hybrid algorithm was established,taking into account the torrential flood causing factors,the disaster-prone environment,and the hazard bearing bodies.Six evaluation indicators were selected,including accuracy,sensitivity,specificity,F-score value,Kappa coefficient,and subject working characteristic curve.The learning vector quantization algorithm was used to quantify the impact of torrential flood impact factors on the occurrence of torrential flood disasters,and the PSO-SVM hybrid algorithm model was compared with single algorithm models.The results show that the hybrid algorithm has a certain transfer ability and can more accurately reflect the spatial distribution characteristics of torrential flood risk.The working characteristic curve indicators,Kappa coefficient,and accuracy of the validation set subjects were 0.934,0.833,and 0.912,respectively.The PSO-SVM hybrid algorithm model can significantly improve the accuracy of torrential flood risk assessment.