A Combined Qualitative and Quantitative Prediction Scheme for Cold-Wet Extreme Weather in Guangxi Based on Intelligent Computing

Taking the cold-wet index measuring chilling damage in winter as the predictand, a qualitative, discriminant prediction model has been developed for daily cold-wet extreme weather forecasting in winter. This model is based on a Random Forest (RF) algorithm, and uses daily temperature and precipitation data, NCEP/NCAR reanalysis data, and 24-h forecast field data. Further, a new, nonlinear, intelligent computing, quantitative ensemble prediction scheme has been developed for predicting cold-wet extreme weather in Guangxi by employing Particle Swarm Optimization (PSO) algorithm, and it is termed the PSO-FNN ensemble prediction model. The ensemble members of the PSO-FNN model were generated by adopting the PSO algorithm. Results showed that the qualitative and quantitative comprehensive prediction based on different intelligent computing methods proposed in this paper were in accord with the forecast characteristics of the small probability extreme weather event. Threat score of the qualitative forecast model based, on the RF algorithm for cold-wet extreme weather in Guangxi, was 0.77, false alarm rate was 0.23, missing rate was 0, equitable threat score was 0.41, and the true skill statistic was 0.53. Moreover, the forecast accuracy of the quantitative PSO-FNN ensemble prediction model was higher than those of the linear and nonlinear forecast models. Using identical modeling samples and independent samples, the PSO-FNN ensemble prediction model showed the reduction in mean absolute errors being >25% relative to the stepwise method, and 14.37% relative to the normal fuzzy neural network. Analyses of the new scheme suggested that the forecast accuracy of the ensemble prediction model was improved by enhancing the prediction ability and population diversity of the individual ensemble members. Therefore, the generalization capacity of the intelligent computing ensemble prediction model was significantly enhanced, and the forecast results were stable and reliable, providing new forecasting tools and prediction modeling methods for objective forecasts of cold-wet extreme weather.