Abstract: Abstract： With the development of science and technology， human engineering activities in mountainous areas are becoming increasingly frequent， which exacerbating the frequency of flash floods. Accurately and timely predicting the possibility of mountain flood disasters is of great significance for ensuring engineering safety， reducing economic losses， and improving personnel safety prevention capabilities. The application of artificial intelligence algorithms in predicting mountain flood disasters has become the focus of current researchers. In order to solve the problems of insufficient prediction accuracy caused by sensitivity differences in triggering factors of mountain floods， suboptimal model fitting effect caused by small sample data， and difficulty in determining nonlinear model parameters， the principal component analysis and ν support vector machines are combined for predicting flash floods， using artificial fish swarm algorithm to expand the search range and speed of particles in particle swarm algorithm， and using improved particle swarm algorithm to optimize support vector machine parameters， AFSPSO-ν-SVM probability prediction model for mountain flood disasters is established. Through experiments， the proposed model was compared with BL models， ν-SVM model， PSO-ν-SVM model. The results of experiment show that the proposed model has the smallest error and the fastest speed. The paper provides a new approach for research in the field of flash flood forecasting and warning.

Key words: Key words： artificial fish swarm algorithm, particle swarm algorithm, support vector machine, mountain torrent disaster, prediction model