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With the progress of information technology such as computer application technology and remote sensing technology, real-time meteorological disaster monitoring and early warning and emergency decision-making based on remote sensing data have become possible. There have been a lot of studies on NN (Neural Network) forecast modeling and climate analysis in intelligent early warning, but in meteorological forecast modeling, it is difficult to determine the initial weight and threshold value by using NN method, and the determination of network structure and parameters needs to be trained for many times, which is likely to cause the problem of over-fitting, which greatly affects the generalization ability of the network. In addition, there are local minima in the NN model, which cannot guarantee prediction accuracy and are not conducive to the real-time and effective tracking and prediction of the weather system. Therefore, this paper uses GA (Genetic Algorithm) to optimize the structure of NN and its connection weight and gives the concept of intelligent genetic arithmetic. That is, combining genetic arithmetic and BP arithmetic to optimize the weight of NN, so as to avoid the calculation results into local optimization, and reduce the number of iterative operations, improve the speed of solution. In this article, genetic arithmetic is applied to the meteorological disaster risk warning system, and the NN warning model based on the optimization of genetic arithmetic is established. The experiment result expresses that the average relative error of the intelligent GA early warning model is 0.43%, and the prediction accuracy has obviously improved. This shows that the method is feasible and effective for complex systems. Intelligent genetic arithmetics also have their advantages, and the predictive ability of NN is enhanced.
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