Application of CNN-LSTM optimization model in predicting tailing dam saturation lines
The saturation line plays a vital role in maintaining the operational stability of tailings dams.However,accurately predicting its evolution remains a significant and challenging task.To address this issue effectively,this paper proposes a high-precision Convolutional Neural Network-Long Short-Term Memory(CNN-LSTM)optimization model specifically designed for predicting the saturation line of tailings dams.The paper focuses on a copper mine tailing pond in Yunnan Province as the research subject.It extracts saturation line data from various monitoring points on the tailings dam.Initially,CNN is employed to capture the interdependencies among saturation line data points in high-dimensional space.Subsequently,convolution and pooling operations are used to input spatial correlation features of the saturation line's temporal characteristics into the LSTM layer.Secondly,leveraging LSTM's ability to capture and model long-term dependencies,the model selectively stores and manages the CNN-extracted features,ensuring both short-term and long-term memory of the saturation line's temporal characteristics.Secondly,LSTM is utilized to capture and analyze long-term dependencies within the features extracted by CNN,enabling selective storage and management of both short-term and long-term memory.During computation,the optimal combination of CNN convolutional layers and LSTM hidden layers is optimized to achieve accurate prediction of the saturation line trend.The experimental results demonstrate that the model exhibits rapid convergence and robust generalization capabilities compared to single CNN and LSTM models.The determination coefficient,average absolute error,mean square error,average absolute percentage error,and root mean square error of the prediction model achieved exceptionally high levels of fitting accuracy and prediction precision.This demonstrates the superiority of the CNN-LSTM optimization model in predicting the saturation line of tailings dams,providing a crucial foundation for ensuring the long-term safe operation of tailings ponds.
safety engineeringtailings damConvolutional Neural Network(CNN)Long Short-Term Memory(LSTM)predictions of saturation line
万方数据
维普
