Study And Application Of Support Vector Machine In The Analysis Of Earth Rock Dam Safety Monitoring Data

As the most practical dam type,earth and rock dam is widely used in the hydropower industry.Due to its own advantages and disadvantages,its safety needs to be attached great importance to.In the world,people adopt various methods to maintain its safety,and its safety prediction has become one of the important studies on the maintenance of earth and rockfill dams.If reliable and accurate prediction methods are adopted,the safety prediction method will be adopted.In order to ensure the safe operation of earth and earth-rock dams,it is very important to study the analysis method of the safety monitoring data of earth and earth-rock dams The use of reliable analysis method can provide basis for the later operation and management,and also reduce the potential safety hazards of earth and earth-rock dams.Compared with traditional prediction methods,support vector machines have stronger generalization ability in small sample and nonlinear prediction,and have been widely used in face recognition,medical diagnosis,text classification,remote sensing image analysis,time series prediction and other fields.Based on the study and summary of the status quo of safety monitoring data analysis and support vector machine(SVM)application for earth-rock dams,this paper establishes a seepage prediction model for earth-rock dams based on support vector machine(SVM),and validates and predicts the model by example.In order to further verify the prediction effect,the GA-BP neural network prediction model,which is widely used at present,is used to compare the prediction results of SVM,and the results are verified by the measured data.The results show that the accuracy of SVM prediction model is better than that of GA-BP neural network prediction model,and the prediction accuracy is the highest.