基于残差网络的怒江泥石流沟谷分类及其预测  

Classification and Prediction of Nujiang Mudslide Valley Based on Residual Network

作　　者：刘存熙 王保云 LIU Cunxi;WANG Baoyun(School of Mathematics,Yunnan Normal University,Kunming 650500,China;Key Laboratory of Complex System Modeling and Application in Yunnan Province University,Kunming 650500,China)

机构地区：[1]云南师范大学数学学院,云南昆明650500 [2]云南省高校复杂系统建模及应用重点实验室,云南昆明650500

出　　处：《现代信息科技》2022年第12期108-110,114,共4页Modern Information Technology

基　　金：国家自然科学基金(61966040)。

摘　　要：云南的怒江流域是泥石流的高发地区。基于怒江流域的沟谷数字高程模型(DEM)图,采用残差神经网络对沟谷图像进行训练,实现分类预测,并使用损失值、正确率对模型进行评价。结果表明:利用残差网络对沟谷数字高程模型(DEM)图进行分类及预测可以达到60%的准确率。该研究结果可为怒江流域泥石流预警及防治工作提供理论参考。The Nujiang River Basin in Yunnan is a high-incidence area of mudslides.The valley Digital Elevation Model (DEM) map based on the Nujiang River Basin uses the residual neural network to train the valley images to achieve classification prediction,and the loss value and the accuracy rate are used to evaluate the model.The results show that the classification and prediction of the valley Digital Elevation Model (DEM) map can reach the accuracy rate of 60% by using the residual network.The research results can provide a theoretical reference for the early warning and prevention work of mudslides in the Nujiang River Basin.

关 键 词：泥石流 数字高程模型 残差神经网络 预测 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP391.4[自动化与计算机技术—控制科学与工程]