摘要
针对地质灾害气象风险预警精细化程度不够的问题,本文基于智慧黄山时空信息平台,对数字正射影像、数字高程模型、地质灾害点数据库、气象降雨量、历史降雨量、2003年以来的地质灾害数据、720°全景影像等多源数据进行有效集成,形成地质灾害预警大数据,分析历史灾害点与降雨量之间的关系,将降雨量列为诱发地质灾害的重要因子,构建临界降雨判据模型,开发地质灾害预警系统。通过系统自动生成预警信息,并将预警信息发送至区县、乡镇和重要地质灾害隐患点防灾责任人和监测人,实现预警时段由24小时缩短为1小时、预警范围由以区县为单位缩小为以乡镇为单位,有效减轻防灾成本,提高预警时效。
Aiming at the lack of meteorological risk early warning precision of geological disasters,based on the intelligent Huangshan spatio-temporal information platform,this paper effectively integrates multi-source data such as digital orthophoto map,digital elevation model,geological disasters database,meteorological rainfall,historical rainfall,geological disasters data since 2003,720°panoramic image and other multi-source data to generate geological disasters early warning data,analyzes the relation between historic disasters and rainfall and lists rainfall as the important factor initiating geological disasters,finally constructs the critical rainfall model and develops geological disasters early warning system.Based on the analysis of the historical data of Huangshan city,an early warning model of geological disasters in line with the situation of Huangshan city is constructed.The early warning information is automatically generated by the system,and the information is sent to the person in charge of disasters prevention and monitoring in districts and counties,towns and important geological hazard points.The early warning period is shortened from 24 hours to 1 hour,and the warning range is narrowed from districts and counties to towns,which can effectively reduce the cost of disasters prevention and improve the timeliness of early warning.
作者
马霖
程斌
MA Lin;CHENG Bin(Land Resource Center of Huangshan,Huangshan 245000,China;Huangshan Geological Environment Monitoring Center,Huangshan 245000,China)
出处
《国土资源信息化》
2021年第3期40-45,共6页
Land and Resources Informatization
基金
安徽省自然科学基金项目青年项目“基于实时雨量大数据的黄山市地质灾害定向预警模型研究”(2008085QD170)。
关键词
时空信息
地灾防治
实时定向预警
大数据
spatio-temporal information
geological disasters prevention and control
real-time directional early warning
big data
