Prediction method of landslide geological hazard based on AdaBoost model

Abstract

Aiming at the problem of landslide geological disaster prediction, based on the analysis of geological structure and historical disaster data of a county in southwest Shaanxi, this paper puts forward a landslide geological disaster prediction model based on AdaBoost. Firstly, the data of landslide geological disasters are analyzed, and the main factors of landslide geological disasters are determined by principal component analysis and Spearman grade correlation coefficient method. Then, the main factors are used as feature data to train the model, and grid search is used to optimize the super parameters in the model. Finally, experiments on real data sets show that the prediction accuracy of AdaBoost model is 1.5% higher than the best prediction results of decision tree, logistic regression, support vector machine and k-nearest neighbor method, and AUC is 0.24% higher. It further verifies the effectiveness and feasibility of the model, which can provide a scientific basis for the prediction of landslide geological disasters.