基于CNN-BiLSTM-Attention的三峡库区滑坡地表位移预测研究
Research on predicting surface displacement of landslides based on CNN-BiLSTM-Attention in the Three Gorges reservoir area
陈欢 1冯晓亮 1刘一民 2赵晗 2刘洋 3郭浪 4张军4
作者信息
- 1. 中国地质科学院探矿工艺研究所,四川 成都 611734;自然资源部地质灾害风险防控工程技术创新中心,四川 成都 611734
- 2. 成都工业学院 智能制造学院,四川 成都 611730
- 3. 云阳县地质环境监测站,重庆 404500
- 4. 重庆一零七市政建设工程有限公司,重庆 401120
- 折叠
摘要
地表位移预测在滑坡监测预警中具有重要意义,建立稳定可靠的滑坡位移预测模型是关键.本文基于卷积神经网络和注意力机制的滑坡位移预测方法,并以三峡库区黄泥巴蹬坎滑坡为例进行了验证.本文综合分析了该滑坡长达 8年的降雨量、库水位和地表位移等监测数据,建立了结合卷积神经网络(convolutional neural network,CNN)、双向长短期记忆(bidirectional long short-term memory,BiLSTM)网络和注意力机制(attention)的CNN-BiLSTM-Attention深度学习组合预测模型,采用了适应性学习率和正则化技术进行模型训练,提高了模型的泛化能力同时避免过拟合,并与传统LSTM模型进行对比验证.结果表明:相较于传统的机器学习和神经网络方法,该模型在滑坡位移预测精度上取得了显著提升,预测模型拟合优度(R2)达0.989,平均绝对百分比误差(MAPE)仅为0.059.
Abstract
Surface displacement prediction is of great significance in landslide monitoring and early warning,and establishing a stable and reliable landslide displacement prediction model is crucial.This paper utilizes a convolutional neural network(CNN)and attention mechanism to predict landslide displacement,and takes the Huangniba Dengkan landslide in the Three Gorges reservoir area as an example for verification.This paper comprehensively analyzes the landslide's monitoring data on rainfall,reservoir water level,and surface displacement for 8 years.It establishes a CNN-BiLSTM-Attention deep learning combination prediction model,and uses adaptive learning rate and regularization techniques for model training,improving the generalization ability of the model while avoiding overfitting.Additionally,the model is subjected to comparative validation with the traditional long short-term memory(LSTM)model.The results show that the model's landslide displacement prediction accuracy has been significantly enhanced compared to traditional machine learning and neural network methods.The prediction model's goodness of fit(R2)reaches 0.989,and the mean absolute percentage error(MAPE)is merely 0.059.
关键词
滑坡监测/地表位移/注意力机制/预测模型/三峡库区Key words
landslide monitoring/surface displacement/attention mechanism/predictive model/Three Gorges reservoir area引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据