This invention provides a method for predicting the
dominant frequency of
blasting vibration by integrating parameter fluctuation
processing and the Dream Optimization
Algorithm (DOA), belonging to the field of underground
engineering safety control technology. The
system includes a
data acquisition and uncertainty modeling module, a data preprocessing and feature construction module, a
hyperparameter optimization module, and a prediction model training and result output module. The method obtains relevant parameters through on-site investigation and monitoring, and generates an extended sample set using a combination of probabilistic perturbation modeling and fuzzy triangular modeling. The data is preprocessed and features are selected. The DOA
algorithm is used to globally search the hyperparameters of the Support Vector Regression (SVR) model, and the optimal
hyperparameter combination is selected by combining a robustness
fitness function. The SVR model is trained, and prediction results and uncertainty prediction intervals are generated. This invention can explicitly characterize the uncertainty of geological parameters, achieve efficient
global optimization of hyperparameters, improve prediction accuracy, robustness, and generalization ability, and is applicable to different geological conditions and blasting scenarios. It can be extended to various blasting dynamic response prediction tasks.