In recent years,classical statistical models and machine learning models have parallelly developed in dam safety mo-nitoring field.However,the'predictive ability'of the former and the'interpretability'of the latter usually have certain limitations,and there are relatively few comparative studies on the impact of quantitative multiple factors on dam monitoring measured data.Based on the prototype monitoring data of horizontal displacement and vertical displacement of GTX gravity dam on the tributary of Minjiang River,this paper used multiple linear regression(MLR),partial least squares regression(PLS)and random forest algo-rithm(RF)to establish different dam deformation monitoring models that takes both predictive ability and interpretability into ac-count.At the same time,the feature importance analysis was carried out for each model to explore the influence of different factors on dam deformation.The results showed that the random forest model had the best fitting ability and the partial least squares re-gression model had the best prediction ability among the three models.The interpretability provided by the three models was basi-cally in line with the actual law,and the order of feature importance was consistent:the water pressure component and the temper-ature component had a significant impact on the displacement of the dam body,and the proportion of the aging component was the lowest.The research results can provide reference for the subsequent optimal selection of dam safety monitoring model.
维普
万方数据