基于多源数据地质灾害监测预警应用研究OA

To enhance the application capability of multi-source data in geological hazard monitoring and early warning,this study constructed a monitoring and early warning system that integrates multi-source data such as UAV photography,geological sensors,satellite remote sensing and other multi-source data.By calculating data similarity and setting a fusion threshold of 0.8,the system employs a three-layer convolutional neural network with 5×5 convolutional kernel to extract features and performs secondary fusion on data that does not meet the standard,addressing the consistency issue of multi-source heterogeneous data.Additionally,a deep kernel support vector ma-chine(DKSVM)model is proposed,which utilizes deep learning to automatically extract high-order feature associa-tions such as terrain slope and soil moisture,and combines the structured risk minimization principle of support vector machine to achieve classification prediction.The study selected nine types of geological hazards,including landslides,debris flows,and ground subsidence,and compared them with the early warning models in referenc-es[3]to[4].The results indicate that among the nine types of geological disasters such as landslide,debris flow and ground subsidence,the accuracy of disaster prediction of this system reached 98.7%,which is significantly im-proved compared with the comparison literature,with F1 values for landslide,debris flow and other disaster types exceeding 97.6%.After data fusion,the proportion of effective information increased from 65%to 92%,with an average similarity of 0.89,verifying the robustness and generalization ability of the model in complex geological en-vironments,and providing a technical reference for intelligent geological disaster early warning.