水工结构变形预测模型构建与解释
Forecasting and analysis of deformation in hydraulic structures
胡江 1苏荟2
作者信息
- 1. 南京水利科学研究院 水灾害防御全国重点实验室, 江苏 南京 210029;水利部大坝安全管理中心, 江苏 南京 210029
- 2. 南京水利科学研究院 水灾害防御全国重点实验室, 江苏 南京 210029;河海大学 水利水电学院, 江苏 南京 210098
- 折叠
摘要
现有基于机器学习算法的水工结构安全监控模型结果的可解释性较差.为提高安全监控模型的可解释性,发展一种基于集成学习算法的水工结构变形预测模型构建与解释方法.简述改进统计模型及随机森林(RF)、极端梯度提升树(XGBoost)两种常用的集成学习算法,引入沙普利值可加性解释(SHAP)方法实现集成学习算法模型结果的可解释性,阐述SHAP方法的原理和推导过程.以某运行初期特高拱坝变形数据为例验证方法的有效性和实用性.结果表明,XGBoost模型具有较高的预测精度,预测集决定系数大于 0.982,改进统计模型精度次之,RF模型精度相对较低;SHAP方法可以分离不同自变量对效应量的影响大小,并能给出全局和局部的影响机制,实现模型拟合和预测结果的可解释性.提出的方法综合了"机理驱动"和"数据驱动"模型的优势,可为水工结构运行管理提供决策参考.
Abstract
The current machine learning-based safety monitoring models for hydraulic structures lack interpretability in their results.To address this issue,we propose a prediction and interpretation method for dependent variables based on ensemble learning algorithms.We provide a brief description of an improved statistical model and two commonly used ensemble learning algorithms,namely random forest(RF)and extreme gradient boosting tree(XGBoost).Additionally,we introduce the shapley additive explanation method(SHAP)to achieve interpretability in the results of ensemble learning algorithm models.We explain the principles and derivation process of the SHAP method.To verify the effectiveness and practicality of our approach,we utilize deformation data from a super high arch dam during its initial operation period as an example.The results demonstrate that the XGBoost model exhibits high prediction accuracy,with a decision coefficient greater than 0.982 in the prediction set.It is followed by the improved statistical model,while the RF model shows relatively poorer accuracy.The SHAP method effectively isolates the influence of different independent variables on the dependent variable,providing an impact mechanism and enhancing the interpretability of the fitting and prediction results.Our proposed method combines the strengths of both"mechanism driven"and"data driven"approaches,offering valuable insights for the operation and management of major hydraulic structures.
关键词
水工结构/变形/安全监控/机器学习/集成学习算法/可解释性Key words
hydraulic structure/deformation/safety monitoring/machine learning/ensemble learning/interpretability引用本文复制引用
基金项目
国家自然科学基金(52179138)
国家自然科学基金(51879169)
国家自然科学基金(52209165)
出版年
2024
NETL
NSTL
维普
万方数据