Long2term rainfall forecasting based on random forest
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Abstract:
Random for est is an algo rithm w hich has o bv ious advantag es in dea ling w ith la rge data set based on classificatio n tr ee and pro po sed in this century In this paper, random for est w as applied to pr edict t he long2term precipitat ion, the Yangt ze River reg ioncs precipitatio n in January is taken as an ex ample, the random forest is used to select the im po rtant facto rs from the 74 at2 mo spheric circulation fact ors and t he precipitation mo nthly by The National Climate Center forecast as predictio n factor s and to predict the precipit ation. Besides, the neur al netw or k fo recast ing results ar e compared. Fo recasting results o f the two models, random for est mo del generalization erro r is 13%, forecast accuracy r ate is 75%, w hile the r ate o f neural netw ork accur acy is 67%. Besides, t his study also for ecasted the class of pr ecipitatio n of the flood season in the middle and low er reaches of the Yan2 g tze r iver region T he results showed that random forest is wo rthy of further research and applicat ion as the simulat ion and fo re2 casting of the lo ng2term pr ecipitation is r elatively go od.
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