基于机器学习的大坝变形预测接口服务设计与实现OA

The safety of dams serves as the primary guarantee in the construction and management of hydraulic engineering,playing a crucial role in flood control,drought resistance,ecological environment protection,social and economic stability,and the utilization of water resources.The deformation of a dam can visually represent changes in the structure,and by providing an efficient and accurate dam deformation prediction model,it can effectively monitor and respond to operational risks,enabling early warnings and reducing the likelihood of disasters.At present,research on machine learning in dam deformation prediction mainly focuses on improving prediction accuracy and optimizing theoretical models,while studies on practical applications are relatively insufficient.Therefore,this paper combined the relevant business scenarios of digital twin hydraulic engineering construction and engineering safety management,exploring how machine learning models can be applied to practical projects.The goal of constructing a digital twin platform for hydraulic engineering is to make sure that the dam deformation prediction model can provide efficient and stable application programming interface(API)services,ensuring that the business functions of the platform can call the interface at any time.Based on the above analysis,a RESTful API design scheme was proposed,focusing on key technical aspects such as data cleaning,model selection,parameter optimization,model serialization,Flask service setup,and model performance evaluation.The settlement data from a concrete-faced rockfill dam at a certain reservoir was utilized to conduct deformation analysis,wherein support vector machine(SVM),extreme gradient boosting(XGBoost),and echo state networks(ESN)models were constructed to predict the deformation of the dam,and their predictive performance was compared against the traditional multiple linear regression(MLR)statistical model.The results demonstrate that the deformation prediction of SVM most closely aligns with the actual settlement trend.By employing the Taylor diagram to comprehensively evaluate the prediction performance of various models,it is found that the machine learning models,compared to the mathematical statistical model,exhibit smaller deviations between the predicted values and the actual monitoring values,which further substantiates the significance and research value of machine learning models in the practical application of deformation prediction,highlighting their potential for more accurate and reliable deformation prediction in real scenarios.However,given the wide variety of machine learning models,each with different data-fitting characteristics,the model service interface should integrate multiple models to adapt to monitoring data with varying features,thereby ensuring the accuracy and reliability of the prediction results.This paper implements the deformation prediction function of dams based on machine learning through an HTTP protocol interface,which has been initially applied and verified in practical applications.