基于EBWO-LightGBM的高心墙堆石坝变形预测研究

Deformation prediction model of high-core rockfill dam based on EBWO-LightGBM

摘要

高心墙堆石坝的变形情况是评估大坝工作状态和安全状况最直观可靠的指标之一。准确预测大坝变形，揭示其变化的规律，为调整施工计划或改进大坝安全管理策略提供了关键依据。随着监测技术的发展，大坝布置的监测仪器数量以及采样频率有所增加，大量数据亟待处理，这给大坝变形预测模型带来不小的挑战。为了高效处理大规模数据且避免变形预测模型陷入局部最优，利用改进白鲸优化算法(Enhanced Beluga Whale Optimization, EBWO)对轻量级梯度提升机(Light Gradient Boosting Machine, LightGBM)的超参数进行寻优，以获得最优超参数组合，构建基于EBWO-LightGBM的高心墙堆石坝变形预测模型。以西南某高心墙堆石坝为例，分别建立EBWO-LightGBM模型、LightGBM模型、ELM模型、SVM模型和RF模型并对其预测结果进行分析，EBWO-LightGBM预测模型的相关系数(R²)高达98.2%,较LightGBM提升3.81%,均方根误差(RMSE)减小了11.72%,表明该模型可以较好地平衡全局和局部性能，提升模型预测性能，与ELM、SVM、RF相比，该模型R²提升了4.80%、4.58%和4.25%,RMSE减小了13.41%、13.18%和12.75%,验证了该模型的优越性。

Abstract

The deformation of high-core rockfill dams is one of the most direct and reliable indicators for assessing the operational state and safety conditions of dams. Accurately predicting dam deformation and uncovering its patterns are crucial for adjusting construction plans or improving dam safety management strategies. To address the issues of local optima entrapment and handling large-scale data in dam deformation prediction models, the Enhanced Beluga Whale Optimization(EBWO) algorithm is utilized to optimize the hyperparameters of the Light Gradient Boosting Machine(LightGBM), aiming to obtain the optimal hyperparameter combination. This forms the basis for constructing a deformation prediction model of high-core rockfill dams based on EBWO-LightGBM. Taking a high-core rockfill dam in the Southwest as a case study, EBWO-LightGBM, standard LightGBM, ELM, SVM, and RF models were developed, and their predictions analyzed. The EBWO-LightGBM prediction model achieved a correlation coefficient(R²) of 98.2%, an improvement of 3.81% over standard LightGBM, and a reduction in Root Mean Square Error(RMSE) of 11.72%. This demonstrates that the model effectively balances global and local performance, enhancing predictive capabilities. Compared to ELM, SVM, and RF models, the EBWO-LightGBM model showed an increase in R² by 4.80%, 4.58%, and 4.25% respectively, and a decrease in RMSE by 13.41%, 13.18%, and 12.75%, confirming its superiority.

关键词

高心墙堆石坝 / 变形预测模型 / 改进白鲸优化算法 / 轻量级梯度提升机

Key words

high-core rockfill dam / deformation prediction model / EBWO / LightGBM

Author summay

李子健(1999—), 男, 硕士研究生, 主要从事水工结构安全监控研究。E-mail: [e-mail]

基金资助

国家自然科学基金青年基金项目(52309165)