The application discloses a kind of based on BEO-XGBoost
algorithm's side
slope stability evaluation method, and the invention includes the following steps: S1, the factor that influences side
slope stability is analyzed, determines the parameter for being used for analyzing side
slope stability, constructs side slope dataset;S2, the data distribution situation is analyzed, and
data set is handled;S3, establishes XGBoost model, and
data set is divided into
training set and
test set;S4, constructs BEO model and optimizes XGBoost;S5, BEO-XGBoost model is trained and tested.The BEO
algorithm used in the application is a powerful
heuristic algorithm developed based on the biological behavior of black eagles, which simulates the hunting, migration and breeding behavior of black eagles.Through simulating these behaviors of black eagles, BEO can gradually find the optimal solution from a complex search space, which makes BEO suitable for optimizing complex nonlinear problems such as
machine learning models.XGBoost is an ensemble algorithm based on decision trees, which uses
gradient descent to optimize the residual error of each tree, thereby continuously improving the accuracy of the model.Using the BEO-XGBoost model to predict the stability of the slope provides a new and highly adaptable
hyperparameter optimization strategy that combines the global and local search mechanisms of BEO and significantly improves the accuracy of slope stability prediction through dynamic adaptive search.