Research on meteorological prediction with Bayesian classifier
Haiqing Zhao · Jisuanji gongcheng yu sheji · 2007
Applying the theory and methods of machine leaning in meteorological prediction,research of rainfall amount classification is conducted based on Bayesian deducing and learning theory,an algorithm named learn-and-classify--rainfall is presented for predicting rainfall amount with Na?ve Bayesian classifier to improve prediction accuracy.Firstly,various predictors and predicting objectives are categorized according to meteorological standards,secondly,prior probabilities of prediction objectives and conditional probabilities of predictors on the history meteorological data which is taken as training set are learned,lastly,it calculates maximum a posteriori probability hypotheses with NBC as the rainfall classification objective.The experiments shown that it is practicable and robust and effective and easy to be realized,and the results also suggested it acquired better accuracy rate than many other existing prediction methods such as re-gression analysis and cluster analysis which are adopted in short term meteorological prediction at present.At the same time,it can offer an instruction for selecting predictors,which often puzzles the researchers in the field of meteorology.