基于机器学习组合模型的引水工程高边坡位移预测研究OA

To establish a reliable displacement prediction model for monitoring and early warning of high slopes in water diversion projects,the NeuralProphet model was introduced into high slope displacement prediction.Its trend module and autoregressive module were utilized to accurately learn the displacement patterns of high slopes in water diversion projects.The Optuna framework was employed to optimize the hyperparameters of the NeuralProphet model,leading to the development of the Optuna-NeuralProphet high slope displacement prediction model for water diversion projects,achieving accurate prediction of high slope displacement patterns.Taking the high slope in the Jianghuai communication section of the Yangtze-to-Huaihe water diversion project as the research subject,displacement monitoring data were trained and predicted,and comparative validation was conducted with the NeuralProphet model.The results indicated that the constructed Optuna-NeuralProphet model exhibited high accuracy and can more effectively predict displacements,providing a new approach and method for high slope displacement prediction in water diversion projects.