A linked multi-device and multiparameter landslide early warning method
In landslide monitoring and early warning systems,the traditional early warning method that only considers a single parameter of a single device has low accuracy and produces false alarms.Therefore,to obtain accurate and reliable early warnings,a collaborative,multiparameter,multi-device early warning method is established for landslides based on multiple sets of monitoring devices.The multiparameter,early warning method uses multiple parameter indicators to process the monitoring data from each device comprehensively,considering the weights of each model to determine the early warning level of each device.The collaborative,multi-device,early warning method divides the devices into different groups according to their spatial distribution,determines the weights of each group based on landslide mechanisms,and adjusts the weights according to the characteristic attributes of the monitoring devices.Based on a comprehensive analysis of the early warning conclusion from each device,the proposed method analyses the overall deformation evolution of the landslide and determines an overall early warning level.The application of this method can quickly and accurately provide information on the precursors of instability and failure in various regions,overall information of the landslide itself,and issue early warnings in a timely manner.Taking the Huangshuigou landslide in Cangsong Village,Shalong Township,Xiaojin County,Sichuan Province as a prototype case,the application effect of the method is tested.The results show that compared with the single-parameter,single-device early warning method,the proposed method reduces the number of early warnings from 32 to 11,the number of false alarms from five to one,and increases the accuracy of early warning from 84％to 91％.This case study shows that this method can improve the effectiveness and accuracy of early warnings and it can make better disaster prevention decisions.
landslidemonitoring and warningmonitoring equipmentmonitoring parameterswarning model
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