Short-term rainfall multi-mode integrated forecasting based on machine learning models

H igh accuracy o f sho rt2term rainfall for ecast ing is of g reat impo rtance fo r flood for ecasting and reserv oir operat ion. It can not only improv e the accuracy o f floo d for ecasting but also make t he r eser voir operation mo re scient ific and reasonable. Based o n the pr edicted rainfall of the Huanren reservo ir basin using ECMWF, CMA and NCEP in the T IGGE datasets, the artif icial neural netw or k ( ANN) , support v ect or machine ( SVM) and ext reme lear ning machine ( ELM) mo dels w ere dev eloped to simulate and forecast the rainfall o f Huanren reservo ir basin in the nex t 1 to 3 day s, and the effect o f the forecasting results wer e analyzed fr om the aspects of mean absolute err or ( MAE) , roo t mean square error ( RMSE) , Bias, Nash2Sutcliffe efficiency co efficient ( NSE) and predictio n accur acy. Results showed that the int eg rat ed forecasting mo dels based o n SVM and ELM wer e better than the sing le mo dels, and the integ rated models based on ANN wer e better t han the sing le mo dels w hen the input facto rs w ere selected pr operly. Amo ng the thr ee integr ated models, SVM model had the mo st o bv ious impr ovement in ra infall forecasting accuracy, which indicated that the multi2mo del rainfall int eg rat ed fo recast ing method based o n machine learning model was feasible and co uld impr ov e the accuracy o f sho rt2term rainfall fo recast ing .

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