Prediction Method of Mountain Flood Disaster Based on AFSPSO-ν-SVM
With the development of science and technology,human engineering activities in mountainous areas are becoming in-creasingly frequent,which exacerbating the frequency of flash floods.Accurately and timely predicting the possibility of moun-tain 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 be-come the focus of current researchers.In order to solve the problems of insufficient prediction accuracy caused by sensitivity dif-ferences 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 pre-dicting flash floods,using artificial fish swarm algorithm to expand the search range and speed of particles in particle swarm algo-rithm,and using improved particle swarm algorithm to optimize support vector machine parameters,AFSPSO-ν-SVM probabil-ity 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.
artificial fish swarm algorithmparticle swarm algorithmsupport vector machinemountain torrent disasterpre-diction model
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