Forecasting direct economic losses of marine disasters in China based on a novel combined model

Abstract The accurate prediction of marine disaster losses is necessary to improve the efficiency of policy makers and researchers. This paper proposes a novel model, i.e., the ENN-GRNN-DI model, combining the Elman neural network with a generalized regression neural network and definite integral to forecast the direct economic losses of marine disasters. The proposed combined model not only makes full use of the data information, but it also achieves interval prediction that obviously increases the reliability and enhances the forecasting performance. To evaluate the performance, a comparison of the proposed combined model and the conventional models is provided. The experimental results show that the novel combined model outperforms the benchmark models in forecasting the direct economic losses of marine disasters.