An Ensemble Forecast Method of Rainstorm Based on mRMR and Random Forest algorithms

4th International Conference on Advances in Energy Resources and Environment Engineering

能源学;生态环境科学

Zhao, Hua-Sheng^1 ; Huang, Xiao-Yan^1 ; Huang, Ying^1

Guangxi Research Institute of Meteorological Disasters Mitigation, Nanning, China^1

关键词: Ensemble forecast methods;    Ensemble methods;    European centre for medium-range weather forecasts;    FORECAST model;    Forecast products;    Interpolation method;    Maximum relevance minimum redundancies;    Random forest algorithm;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/237/2/022006/pdf
DOI  :  10.1088/1755-1315/237/2/022006

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

This thesis presents an ensemble method of rainstorm based on maximum relevance minimum redundancy (mRMR) and random forest algorithms called mRMR-RFR. The proposed method is applied to the forecasting results of ensemble numbers from European Centre for Medium-Range Weather Forecasts (ECMWF). The method filtrates the 51 collected forecast members of ECMWF using the mRMR algorithm, and selects the members with maximum relevance and minimum redundancy to the forecasting objects. The selected members are regarded as the input factors of the random forest algorithm for forecast modeling. Experimental forecasting statistics show that, compared with the interpolation method of the collected forecasting members of ECMWF, mRMR-RFR can result in better forecast effect and use the numerical forecast products more effectively. The proposed method is thus suitable for forecasting.