Research on big data mining and intelligent optimization algorithms in geohazard prediction and pre-alarm

With the increasing influence of geohazards on human society, improving the accuracy of prediction and pre-alarm has become the key. This article focuses on the field of geohazard prediction and pre-alarm, aiming at building an efficient and accurate model. By integrating big data mining and intelligent optimization algorithm, this article first expounds the relevant theories of geohazards and the technical basis of big data mining and intelligent optimization algorithm, and then constructs a model integrating them. Using multi-source geohazard data, after preprocessing, feature extraction and other big data mining steps, combined with intelligent optimization algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO), the model parameters are optimized. The experimental results show that compared with the traditional decision tree (DT) and support vector machine (SVM) models, this model has remarkable performance advantages, with the accuracy rate of 89.56 1.23%, the recall rate of 87.45 1.56% and the F1 value of 88.49 1.32%. It shows that the model can effectively deal with complex data and provide reliable technical support for geohazard prediction and pre-alarm.