呼和浩特:地质灾害预警信息将通过手机发送

5月1日起,内蒙古呼和浩特市国土资源局启动汛期地质灾害气象预警预报工作,预报预警对象为降雨诱发区域的突发性地质灾害,以泥石流、滑坡和崩塌为主。据了解,地质灾害气象预警预报信息通过手机短信形式发布到市、旗县区乡     

利用飞机摸透台风行踪

直到今天，准确预报台风依旧被认为像是在“钢丝上跳舞”一般困难。怎样才能更好地“捕获”行踪飘忽不定、结构千变万化的台风？前天举行的台风科学讨论会上，专家们呼吁尽快实施飞机“捕风”。其实，很早以来，各国的海洋气象科学家就着手进行对台风的“捕捉”。最早的台风预报可追溯到18世纪初叶，只不过当时收集台风资料只能依靠航行在世界各大洋上的商船开展义务观测才能得到。如今，我国已经建立起遍布全国沿海的地面监测网、高空气象探测网、气象雷达探测网、海洋监测网等多种预警网络。     

文山州天气气候特点及灾害性天气预报着眼点

简述文山州的天气气候特点,对文山州的主要影响系统下的天气特点进行说明,文山州的主要影响系统包括:昆明准静止锋、切变线(两高间辐合)、南支槽、西南涡、西行台风、孟加拉湾风暴。对文山州的主要灾害性天气的预报着眼点进行了分类分析,包括对强降雨、强对流、强冷空气、气象地质灾害的预报,并给出相应预报指标。     

基于GIS的铁路地质灾害信息管理与预警预报系统

地质灾害对我国铁路建设及其正常运营所带来的负面影响日益显著,利用GIS技术对铁路地质灾害进行有效的管理与控制是减少灾害损失的一条重要途径。针对铁路地质灾害信息管理中数据的标准化、可视化、信息化和网络化的发展与要求,基于GIS技术构建的铁路地质灾害信息管理与预警预报系统实现了铁路地质灾害多源海量数据的统一存储与管理,集铁路灾害信息“数据采集-查询检索-预警预报-Web发布”功能于一体,具有较好的智能化与自动化能力。本文介绍了该系统的总体架构和功能特征,并详细讨论了系统实现中的三个关键问题:铁路地质灾害多源海量数据的采集与存储;铁路地质灾害分析评价与预警预报模型与GIS技术的耦合;铁路地质灾害信息Web发布系统的设计。该系统的最终实现及运行将对我国铁路部门减灾防灾产生重要的积极作用。     

国土资源部启动地灾三级应急响应

<正>为进一步防范近期台风及强降雨引发地质灾害,近日,国土资源部启动三级应急响应,要求北京、天津、河北等13个省(市、区)国土资源主管部门扎实做好隐患排查,严格落实防灾责任,进一步加强群测群防、预警预报和应急值守,采取有力措施严防地灾。国土资源部要求,相关省(市、区)国土资源主管部门要把防灾减灾工作摆在更加突出位置,加强     

基于主成分神经网络的台风灾害经济损失评估

本研究建立了浙江省台风灾害直接经济损失评估模型。把浙江省台风灾害直接经济损失资料换算成直接经济损失指数,运用主成分分析法对表示致灾因子、孕灾环境与承灾体的评估因子进行数据处理,提取主成分作为BP神经网络模型的输入,从而建立评估模型。模型历史拟合结果和实际一致。在2007年和2008年影响浙江省的5个台风的实际评估中,强台风"Vipa"灾后评估值比实际值偏大2.16,其余4个台风灾后评估值比实况偏大0.2~0.7,反映了人们对影响大的台风防灾减灾工作的重视和防灾减灾效果。根据台风开始影响时过程风雨预报值进行预评估,过程风雨预报值较准确的台风,预评估结果和灾后评估值一致;过程风雨预报值误差较大的台风,预评估效果较差。因此,该模型可用于实际台风灾害直接经济损失评估,提高台风影响前风雨预报准确率是提高预评估准确率的关键。     

平和县气象-地质灾害监测预报探讨

利用平和县1960-2003年5-10月的气象资料，结合县国土资源局的地质灾害调查资料进行分析、区划。结果表明：地质灾害形成的三大因素中，连续降雨、台风、暴雨是最直接且多发的自然触发因素。因此，应采用各种气象资料，结合地质灾害预报雨量界限值进行等级预测和预警报发布，对及早做好地质灾害的防范工作，具有重要的现实意义。     

地质灾害发生时间和空间的预测预报

运用非线性科学的理论和方法,提出了一整套地质灾害预测预报的理论和方法,包括探索地质灾害发生前兆的加卸载响应比方法、地质灾害发生时间预测的协同学模型以及地质灾害区域预测评价的神经网络方法,并建立了基于GIS的地质灾害区域评价系统。同时,通过对向家坝水电站库区的崩滑地质灾害的统计分析,提出了地质灾害发生规模与发生频率间的幂律规则。     

新疆洪水预报预警中融雪径流模型应用进展

针对全球变化背景下极端升温、暴雪和暖湿化现象以及中国新疆地区融雪洪水灾害风险增大问题,概述了新疆不同类型洪水灾害特征,重点阐述了近年发生频率增加、致灾性强、灾害风险增大,但在新疆未引起重视的融雪洪水的研究进展,对比分析了不同类型融雪径流模型特点和研究现状。综合目前融雪径流模型已有进展和面临的挑战,提出新疆未来研究需考虑融雪径流模型的物理机制和融雪消融过程,以提高预报预警精度。回顾了融雪洪水在新疆的预报预警技术,指出构建高精度预报预警融雪洪水模型所面临的风吹雪、冻土表层雪和雪面雨等关键问题,并提出提升新疆洪水模拟、预报预警、应对突发洪水的综合能力的关键技术,为提升新疆融水洪水预报预警技术提供思路与建议。     

平和县气象-地质灾害监测预报探讨

利用平和县1960～2003年5～10月的气象资料,结合县国土资源局的地质灾害调查资料进行分析、区划.结果表明地质灾害形成的三大因素中,连续降雨、台风、暴雨是最直接且多发的自然触发因素.因此,应采用各种气象资料,结合地质灾害预报雨量界限值进行等级预测和预警报发布,对及早做好地质灾害的防范工作,具有重要的现实意义.     

云南气象灾害特征及成因分析

用1950~1999年气象灾害资料,分析云南气象灾害的主要特征,具有种类多、频率高、重叠交错;分布广、季节性、区域性突出;成灾面积小、累积损失大的特征。指出地理环境、气候、人类活动是形成云南气象灾害的主要原因。特殊的低纬高原、邻近热带海洋、地形地貌复杂、山高坡陡、植被少、降雨集中、地质构造复杂、断裂活动强烈是形成云南气象灾害的地理环境因素。季风强弱与冬夏大气环流差异是决定云南气象灾害的主要气候背景。人口剧增,垦植过度,滥伐森林,水土流失严重指出是加剧云南气象灾害频繁发生和损失严重的主要人为因素,提出了云南气象灾害的防灾减灾对策。     

甘南草地雪灾及其预报

甘南州冬春两季雪灾频繁，危害牧业．论述了雪灾成因和分布规津，分析了降雪天气的大气环流．雪灾造成的牲畜死损７０％是发生在３—４月．据３—４月环流特征．进行了不同强度降雪环流分型，并建立了甘南区域和局地的短中长期预报方程．雪灾预报较准确．     

浙江台风(热带风暴)灾害的若干特点

对1949-1992年的气象、水文和灾害资料分析,浙江台风灾害具有以下几个特点:1.登陆或严重影响浙江的台风日期与天文大潮期相遇机率高,沿海地区潮灾严重;2.台风大风是浙江沿海城市台风灾害危害的主因之一,且其危害具有连锁反应倾向;3.台风灾害对浙江农业的危害一般是直接和继发性灾害叠加而成的;4.台风灾害区的地理分布具有明显的山脉走向性;5.台风重灾年在年际分布上具有阶段性.     

浙江台风（热带风暴）灾害的若干特点

对１９４９－１９９２年的气象、水文和灾害资料分析，浙江台风灾害具有以下几个特点：１．登陆或严重影响浙江的台风日期与天文大潮期相遇机率高，沿海地区潮灾严重；２．台风大风是浙江沿海城市台风灾害危害的主因之一，且其危害具有连锁反应倾向；３．台风灾害对浙江农业的危害一般是直接和继发性灾害叠加而成的；４．台风灾害区的地理分布具有明显的山脉走向性；５．台风重灾年在年际分布上具有阶段性。     

A study on the early-warning technique concerning debris flow disasters

According to the principle of the eruption of debris flows, the new torrent classification techniques are brought forward. The torrent there can be divided into 4 types such as the debris flow torrent with high destructive strength, the debris flow torrent, high sand-carrying capacity flush flood torrent and common flush flood by the techniques. In this paper, the classification indices system and the quantitative rating methods are presented. Based on torrent classification, debris flow torrent hazard zone mapping techniques by which the debris flow disaster early-warning object can be ascertained accurately are identified. The key techniques of building the debris flow disaster neural network (NN)real time forecasting model are given detailed explanations in this paper, including the determination of neural node at the input layer, the output layer and the implicit layer, the construction of knowledge source and the initial weight value and so on. With this technique, the debris flow disaster real-time forecasting neural network model is built according to the rainfall features of the historical debris flow disasters, which includes multiple rain factors such as rainfall of the disaster day, the rainfall of 15 days before the disaster day, the maximal rate of rainfall in one hour and ten minutes. It can forecast the probability, critical rainfall of eruption of the debris flows, through the real-time rainfall monitoring or weather forecasting. Based on the torrent classification and hazard zone mapping, combined with rainfall monitoring in the rainy season and real-time forecasting models, the debris flow disaster early-warning system is built. In this system, the GIS technique, the advanced international software and hardware are applied, which makes the system′s performance steady with good expansibility. The system is a visual information system that serves management and decision-making, which can facilitate timely inspect of the variation of the torrent type and hazardous zone, the torrent management, the early-warning of disasters and the disaster reduction and prevention.     

A study on the early-warning technique concerning debris flow disasters

According to the principle of the eruption of debris flows, the new torrent classification techniques are brought forward. The torrent there can be divided into 4 types such as the debris flow torrent with high destructive strength, the debris flow torrent, high sand-carrying capacity flush flood torrent and common flush flood by the techniques. In this paper, the classification indices system and the quantitative rating methods are presented. Based on torrent classification, debris flow torrent hazard zone mapping techniques by which the debris flow disaster early-warning object can be ascertained accurately are identified. The key techniques of building the debris flow disaster neural network (NN) real time forecasting model are given detailed explanations in this paper, including the determination of neural node at the input layer, the output layer and the implicit layer, the construction of knowledge source and the initial weight value and so on. With this technique, the debris flow disaster real-time forecasting neural network model is built according to the rainfall features of the historical debris flow disasters, which includes multiple rain factors such as rainfall of the disaster day, the rainfall of 15 days before the disaster day, the maximal rate of rainfall in one hour and ten minutes. It can forecast the probability, critical rainfall of eruption of the debris flows, through the real-time rainfall monitoring or weather forecasting. Based on the torrent classification and hazard zone mapping, combined with rainfall monitoring in the rainy season and real-time forecasting models, the debris flow disaster early-warning system is built. In this system, the GIS technique, the advanced international software and hardware are applied, which makes the system's performance steady with good expansibility. The system is a visual information system that serves management and decision-making, which can facilitate timely inspect of the variation of the torrent type and hazardous zone, the torrent management, the early-warning of disasters and the disaster reduction and prevention.     

A study on the early—warning technique concerning debris flow disasters

According to the principle of the eruption of debris flows, the new torrent classification techniques are brought forward. The torrent there can be divided into 4 types such as the debris flow torrent with high destructive strength, the debris flow torrent, high sand-carrying capacity flush flood torrent and common flush flood by the techniques. In this paper, the classification indices system and the quantitative rating methods are presented. Based on torrent classification, debris flow torrent hazard zone mapping techniques by which the debris flow disaster early-warning object can be ascertained accurately are identified. The key techniques of building the debris flow disaster neural network (NN) real time forecasting model are given detailed explanations in this paper, including the determination of neural node at the input layer, the output layer and the implicit layer, the construction of knowledge source and the initial weight value and so on. With this technique, the debris flow disaster real-time forecasting neural network model is built according to the rainfall features of the historical debris flow disasters, which includes multiple rain factors such as rainfall of the disaster day, the rainfall of 15 days before the disaster day, the maximal rate of rainfall in one hour and ten minutes. It can forecast the probability, critical rainfall of eruption of the debris flows, through the real-time rainfall monitoring or weather forecasting. Based on the torrent classification and hazard zone mapping, combined with rainfall monitoring in the rainy season and real-time forecasting models, the debris flow disaster early-warning system is built. In this system, the GIS technique, the advanced international software and hardware are applied, which makes the system’s performance steady with good expansibility. The system is a visual information system that serves management and decision-making, which can facilitate timely inspect of the variation of the torrent type and hazardous zone, the torrent management, the early-warning of disasters and the disaster reduction and prevention.     

中国沿海地区近20年台风灾害风险评价

依据自然灾害系统理论,综合考虑致灾因子和承灾体特征,提出台风灾害风险评价方法。在GIS环境下对中国沿海地区台风灾害危险性、脆弱性和风险进行分析评价。评价结果显示:海南省、上海市和广东省、福建省、浙江省的沿海区域台风灾害危险性较高;北京市、天津市、上海市和江苏省、山东省的大部分地区及广东省、福建省、浙江省、河北省的沿海区域承灾体脆弱性较高;海南省、上海市和广东省、福建省、浙江省的沿海区域台风灾害风险较高;而北京市、天津市以及河北省、辽宁省和山东省的大部分区域台风灾害风险较低。     

Comparative evaluation of geological disaster susceptibility using multi-regression methods and spatial accuracy validation

Geological disasters not only cause economic losses and ecological destruction, but also seriously threaten human survival. Selecting an appropriate method to evaluate susceptibility to geological disasters is an important part of geological disaster research. The aims of this study are to explore the accuracy and reliability of multi-regression methods for geological disaster susceptibility evaluation, including Logistic Regression (LR), Spatial Autoregression (SAR), Geographical Weighted Regression (GWR), and Support Vector Regression (SVR), all of which have been widely discussed in the literature. In this study, we selected Yunnan Province of China as the research site and collected data on typical geological disaster events and the associated hazards that occurred within the study area to construct a corresponding index system for geological disaster assessment. Four methods were used to model and evaluate geological disaster susceptibility. The predictive capabilities of the methods were verified using the receiver operating characteristic (ROC) curve and the success rate curve. Lastly, spatial accuracy validation was introduced to improve the results of the evaluation, which was demonstrated by the spatial receiver operating characteristic (SROC) curve and the spatial success rate (SSR) curve. The results suggest that: 1) these methods are all valid with respect to the SROC and SSR curves, and the spatial accuracy validation method improved their modelling results and accuracy, such that the area under the curve (AUC) values of the ROC curves increased by about 3%–13% and the AUC of the success rate curve values increased by 15%–20%; 2) the evaluation accuracies of LR, SAR, GWR, and SVR were 0.8325, 0.8393, 0.8370 and 0.8539, which proved the four statistical regression methods all have good evaluation capability for geological disaster susceptibility evaluation and the evaluation results of SVR are more reasonable than others; 3) according to the evaluation results of SVR, the central-southern Yunnan Province are the highest susceptibility areas and the lowest susceptibility is mainly located in the central and northern parts of the study area.     

云南滑坡泥石流灾害的气象成因与监测

根据云南滑坡泥石流灾害资料，分析了其形成环境与分布特征，给出了典型降水滑坡泥石流灾害事件，研究了滑坡泥石流灾害与气象环境的关系。云南滑坡泥石流灾害主要出现在盛夏7～8月，主要影响天气系统有切变线、冷锋、西风槽、西南涡、孟加拉湾风暴、南海西行台风和两高辐合区。诱发滑坡泥石流灾害的前期降水类型有3种，即暴雨型，中～大雨型和连阴雨型。得出了云南滑坡泥石流发生的区域临界雨量指标，提出了滑坡泥石流灾害的气象监测预警方法。