Abstract:

Low temperature and overcast rain weather during flowering period is one of the main meteorological disasters of rape in Sichuan Basin. Predicting and studying the low temperature and overcast rain weather can provide scientific basis for disaster prevention and reduction of rape. This study was based on the assessment results of the disaster losses of low temperature and overcast rain during rape flowering period at 101 stations in Sichuan Basin from 1961 to 2020. We divided the 60-year sequence into 5 states based on the disaster loss rate, and used Markov test to screen site sequences and select sequences which satisfied the prediction conditions. We set up four models of superposed Markov chain, weighted Markov chain, improved superposed Markov chain, and improved weighted Markov chain to predict the low temperature and overcast rain disaster during the flowering period of rape based on the station sequence passing the Markov test, and performed backtracking and verification on the predicted results. All four Markov models had certain predictive ability, and the improved model had a significant improvement in overall accuracy compared to before, and the distribution of prediction accuracy for various levels of disasters was more uniform than before. In conclusion, the improved Markov model has better predictive performance.

Key words: Sichuan Basin, rape, low temperature and overcast rain, disaster prediction, Markov model