沉积与特提斯地质2024，Vol.44Issue(3)：572-581,10.DOI:10.19826/j.cnki.1009-3850.2024.08006
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
摘要
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分类
天文与地球科学引用本文复制引用
陈欢,冯晓亮,刘一民,赵晗,刘洋,郭浪,张军..基于CNN-BiLSTM-Attention的三峡库区滑坡地表位移预测研究[J].沉积与特提斯地质,2024,44(3):572-581,10.基金项目
国家自然科学青年基金资助项目"断层面库仑应力变化监测方法的力学机理实验研究"(41804089) （41804089）
中国地质调查局项目"地质灾害监测预警与防治支撑(探矿工艺所)"(DD20230447) （探矿工艺所）
