地质与资源2024，Vol.33Issue(6)：836-845,835,11.DOI:10.13686/j.cnki.dzyzy.2024.06.012
基于信息量耦合卷积神经网络的黄土高原滑坡灾害空间分布与易发性评价
SPATIAL DISTRIBUTION AND SUSCEPTIBILITY EVALUATION OF LANDSLIDE DISASTERS IN LOESS PLATEAU BASED ON INFORMATION-CNN COUPLING MODEL:A Case Study of Jiyuan City in Henan Province
摘要
Abstract
Through field survey and data collection,18 influencing factors involving landform,geology,hydrometeorology,human activities,rock-soil mass properties and vegetation coverage are selected to evaluate the landslide vulnerability in Jiyuan City of Henan Province on the basis of information-convolutional neural network(CNN)coupling model,with GIS spatial analysis to quantify the spatial distribution characteristics of landslide.The results show that the landslide disasters are distributed aggregately in the area,with multiple high value centers of kernel density.The areas with very low,low,medium,high and very high landslide susceptibility account for 45.04％,34.58％,8.67％,9.12％and 2.57％,respectively.The very high and high susceptible areas are characterized by developed faults,fragile geological environment and hydraulic erosion.The highest landslide density is 0.804 per km2,occurring in medium susceptible area.The ROC curve and AUC value indicate that the evaluation results have good accuracy,and the prediction ability of the coupling model is reliable.The top 5 influencing factors of landslide susceptibility analysis are distance from roads,distance from faults,slope aspect,terrain roughness,as well as erosion degree and type.This study may provide scientific basis for the prediction and prevention of landslide geological disasters in cities and towns on the Loess Plateau.关键词
滑坡/易发性评价/信息量/卷积神经网络/地质灾害/黄土高原Key words
landslide/susceptibility evaluation/information method/convolutional neural network(CNN)/geological disaster/loess plateau分类
天文与地球科学引用本文复制引用
邓杰,邓杨,乔少南,王沙沙,孔嘉旭..基于信息量耦合卷积神经网络的黄土高原滑坡灾害空间分布与易发性评价[J].地质与资源,2024,33(6):836-845,835,11.基金项目
中国地质调查局项目"西北黄土高原区地质灾害智能监测预警系统应用示范"(DD20230443) （DD20230443）
中央高校基本科研业务费专项资金-长安大学优秀博士学位论文培育资助项目"滑坡机理与启滑判据"(CHD 300102262713,长安大学) （CHD 300102262713,长安大学）
河南省财政项目"河南省卢氏县1:5万地质灾害风险调查(普查)评价"(豫地灾项目[2021]50号). （普查）
