Dam Deformation Prediction Model Based on FOA-BP-AdaBoost and Its Application
In order to improve the prediction accuracy of dam deformation monitoring and solve the problem that the deformation is affected by many factors,the AdaBoost strong prediction combination model based on fruit fly optimization algorithm(FOA)and BP neural network(FOA-BP-AdaBoost)is proposed,and compared with the prediction accuracy of BP neural network model and FOA-BP neural network model applied to engineering examples.The results show that the strong prediction model integrates the characteristics of global optimization of fruit fly algorithm,local optimization of BP neural network and AdaBoost"optimal selection",and optimizes the prediction effect to the greatest extent;The application of the example confirms the accuracy and effectiveness of the FOA-BP-AdaBoost model in the field of dam deformation prediction.The model has been successfully applied to engineering examples,which can provide reference for similar projects.
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