Design of Fault Diagnosis Model for Geological Disaster Monitoring Equipment Based on K-means-LSTM Combination Algorithm
To improve the accuracy and efficiency of geological hazard early warning,this paper proposes a geological disaster monitoring equipment fault diagnosis model based on K-means clustering and LSTM.Through cluster analysis of geological monitoring data,the model can effectively distinguish equipment in normal and abnormal operating conditions,providing an accurate data basis for subsequent Deep Learning analysis.The LSTM Time Series Analysis part uses clustering results to deeply explore potential patterns and trends in time series data to achieve accurate predictions of equipment fault types and their development trends.Experimental verification shows that the combined model has good application potential in the field of geological disaster monitoring and can provide strong technical support for disaster prevention and reduction.Future research will focus on further improving the accuracy and generalization ability of the model,exploring more algorithm combinations and data processing methods to adapt to more complex monitoring environments,and promoting the automation and intelligence of the monitoring system.
万方数据
维普
