Abstract: Blasting is an effective method to prevent avalanches, but it is difficult to determine the appropriate blasting time, blasting location and blasting energy. This study began by collecting and preprocessing indicator data about avalanches. Then we conducted exploratory data analysis on the data, and found a strong seasonal pattern in avalanche occurrences. Using 80% of the data as the training set and 20% as the test set, the support vector machine, random forest and perceptron neural network models were established, with parameters optimized using the Bayesian optimization algorithm. The results showed that the perceptron neural network achieved the highest accuracy. Subsequently, the three models were integrated according to the loss degree, and three integration strategies were compared. The results showed that the highest accuracy of the SVM-RF-MLP model was 0.952. A basic blasting energy model was then developed, taking into account the changes in mountain height and snow layer density over time. Using historical data, a dynamic avalanche stability blasting energy model was built to identify the distribution patterns in snow layer stability. The data is simulated and verified, and a three-dimensional mountain visualization analysis is performed to obtain the optimal blasting timing, blasting location and blasting energy.

Key words: Bayesian optimization algorithm, SVM-RF-MLP model, dynamic avalanche stability blasting energy model