Information gain-based modular fuzzy neural network to forecast rainstorms

Abstract

Abstract This study considers large-scale heavy rainfall as a forecast object based on the European central numerical forecast model product and uses a nonlinear fuzzy neural network (FNN) intelligent calculation method to establish a short-term forecast model of rainstorms. The information gain method is introduced to the predictor processing of the forecast model. Then the characteristics of many rainstorm predictors are calculated and screened on the basis of feature weight, information is condensed, some non-correlated forecast information variables are extracted, and the network structure of the forecast model is optimized. The modeled samples are determined and reconstructed by setting thresholds, and the modular forecast models of heavy rainfall and weak rainfall are established. The actual forecast results of the 24 h experimental prediction of the independent samples of large-scale rainstorms in Guangxi in 2012–2016 showed that the information gain-based modular FNN rainstorm forecasting model has higher prediction accuracy and a more stable forecasting effect. The various types of scores of 24 h of rainstorm (≧50 mm) at 89 weather stations in Guangxi from 2012 to 2016 are: threat score (TS) is 0.368, ETS: equal threat score (E) is 0.141, hit rate (POD) is 0.296, empty report rate (FAR) is 0.559, forecast bias (B) is 0.671, and HSS skill score (H) is 0.247. Further comparison and analysis of the European Centre for Medium-Range Weather Forecasts (ECMWF) numerical forecasting model forecast results indicated that the new model performed nonlinear intelligence calculated interpretation modeling on ECMWF numerical forecasting model products, and forecasting accuracy is improved to a certain extent compared with that of the original model. Forecasting techniques are positive and have good release effects, thereby improving the rain forecasting ability of ECMWF to a certain extent and providing a better reference value for business forecasters.