水力发电学报
            首 页   |   期刊介绍   |   编委会   |   投稿须知   |   下载中心   |   联系我们   |   学术规范   |   编辑部公告   |   English

水力发电学报 ›› 2024, Vol. 43 ›› Issue (9): 106-123.doi: 10.11660/slfdxb.20240910

• • 上一篇    下一篇

大坝渗压混合预测的STL分解-集成学习模型

  

  • 出版日期:2024-09-25 发布日期:2024-09-25

Hybrid prediction model for dam seepage pressure based on STL decomposition and integrated learning

  • Online:2024-09-25 Published:2024-09-25

摘要: 针对目前大坝渗压预测研究大多未区分影响因素对渗压不同特征成分贡献的差异,降低了模型的可解释性,且现有的预测模型大多采用单一算法,存在难以区分具有高度非线性和非稳态混合特征的渗流压力序列模式等问题,本文提出一种基于STL分解和集成学习策略的渗压可解释混合预测模型。该模型首先通过时间序列分解(STL)将原始渗压时间序列分解为季节项、趋势项和余项,以避免现有模型在渗流压力预测中模式混淆的不足;然后,不同成分的变化特征可采用多策略改进麻雀搜索算法(MSISSA)优化的核极限学习机(KELM)和卷积神经网络组合门控递归单元(CNN-GRU)组成的集成学习模型来识别;此外,还采用单次单因子法(OFAT)分析影响因素对渗流压力不同特征成分的贡献,从而改变输入因素的权重,以提高模型的可解释性。案例分析结果表明,在确保模型可解释性的同时,所提出的混合模型与基于单一算法的模型相比,预测精度平均提高了48.44%;与其他集成预测模型相比,预测精度平均提高了11.42%,验证了所提模型的有效性,为大坝渗流安全监控提供了新的建模方法。

关键词: 大坝渗压预测, STL时序分解, 多策略改进麻雀搜索算法, 集成学习

Abstract: Most of the previous studies on dam seepage pressure prediction did not distinguish the differences in the contributions of influencing factors to different characteristic components of seepage pressure, thereby reducing the interpretability of their models. Most of the previous prediction models in literature adopted a single algorithm and thus suffered such problem as a difficulty in exploring accurately the patterns of seepage pressure sequences with highly nonlinear, non-stationary mixed characteristics. This paper develops an interpretable hybrid prediction model for seepage pressure based on seasonal and trend decomposition using loess (STL) and an integrated learning strategy. First, the model adopts STL to decompose the original seepage pressure time series into seasonal, trend and remainder components, so as to avoid the deficiency of pattern confusion encountered in the previous predictions. Then, it identifies the variability characteristics of different components using an ensemble learning model that consists of a kernel extreme learning machine (KELM) optimized by the multi-strategy-improved sparrow search algorithm (MSISSA) and a convolutional neural network combined with a gated recurrent unit (CNN-GRU). In addition, the One-Factor-At-A-Time (OFAT) method is used to analyze the contributions of influencing factors to the different characteristic components of seepage pressure so as to change the weights of input factors and successfully increase model interpretability. Case study shows that while ensuring model interpretability, this new hybrid model improves prediction accuracy by an average of 48.44% compared to a single algorithm-based model and an average of 11.42% compared to other ensemble prediction models. This verifies the model and provides a new modelling approach for dam seepage safety monitoring.

Key words: dam seepage pressure prediction, STL time series decomposition, multi-strategy improved sparrow search algorithm, integrated learning

京ICP备13015787号-3
版权所有 © 2013《水力发电学报》编辑部
编辑部地址:中国北京清华大学水电工程系 邮政编码:100084 电话:010-62783813
本系统由北京玛格泰克科技发展有限公司设计开发  技术支持:support@magtech.com.cn