In response to the problems of low prediction accuracy caused by the varying sensitivities of debris flow triggering factors in current research,poor model training and prediction performance due to limited dataset samples, and difficulty in determining parameters caused by severe nonlinear processes,an improved kernel principal component analysis(KPCA)algorithm was used to screen out factors with general correlation,combined with broad learning(BL) to establish a debris flow probability prediction model.Then,a particle swarm optimization(PSO)based on sine factors was introduced to optimize the model,and finally,a debris flow prediction model based on KPCA-TFPSO-BL was established.The performance of the classic BL model,KPCA-PSO-BL model,and KPCA-TFPSO-BL model was compared through experiments.The results showed that the root mean square error of KPCA-TFPSO-BL was 4.92,the average absolute error was 4.60,and the training time was 7.22 seconds.This model showed the best comprehensive performance in terms of prediction error and training time.This study provides a new approach and reference for the field of debris flow prediction.