A Nonlinear Heuristic-Optimized Neural Network Modeling Approach for the Prediction of Natural Disasters

Abstract

The disaster early-warning and corresponding prediction based on the nonlinear models is one of the important research aspects in the scope of natural disaster prediction and prevention. In this paper, the principle of applying heuristic algorithms to modify the classical neural networks so that some improved models with more efficiency can be achieved is proposed. The detailed structure and implementation of such model is also provided by building a heuristic-optimized neural network (HNN) model. Some regional flood data of China is then applied to conduct the prediction with the proposed model. Simulation results demonstrate that the proposed model can achieve higher accuracy and find an appropriate trade-off between the cost of processing time and precision, compared to the normal nonlinear models.