区域和沟谷相结合的泥石流预报及其应用

在分析泥石流预报现状和泥石流减灾决策对泥石流预报的要求的基础上,提出了建立区域和单沟相结合的泥石流预报的问题。以泥石流发育的基础数据库、降水的动态预报与监测和泥石流预报模型为基础,以GIS技术为工具,建立泥石流预报平台。泥石流预报模型采用基于模糊数学的泥石流预报模型,预报结果应用概率分级进行表述,以适应泥石流预报准确率低的不足,并最大程度地克服了过分依赖临界降水量进行泥石流预报的不足。将该泥石流预报方法应用到北京山区泥石流预报中,建立了北京山区泥石流预报系统。     

基于水文响应单元的泥石流灾害易发性分区方法

以水文响应单元和栅格单元为基础评价单元,运用因子贡献率法和信息量法两种评价模型对安宁河流域进行泥石流灾害易发性评价。研究结果表明,在流域尺度范围内,水文响应单元能够更好体现区域中的微观综合地貌特征,切合区域整体性描述;基于水文响应单元的易发性分区不仅与历史泥石流实际分布情况的契合度较高,而且数学模型方法适应效率较高和区内孕灾条件体现更为明显。由此,建立基于水文响应单元泥石流灾害易发性分区对探求流域尺度范围内泥石流灾害易发性分区方法与评价制图是一个有益的尝试与拓展,可为制定减灾防灾决策提供科学参考。     

泥石流区域预报中的预报降水处理

预报降水处理是泥石流区域预报的一个重要环节.在介绍了现有的泥石流预报方法的基础上,分析了不同时空尺度泥石流区域预报对预报降水的不同需求.根据精细化降水数值预报产品和多普勒天气雷达降水预报产品的特点,重点讨论了精细化降水数值预报产品用于大尺度短期泥石流区域预报、多普勒天气雷达降水预报产品用于中小尺度短临泥石流区域预报的处理方法,并建立了适应不同时空尺度(大尺度和中小尺度)的泥石流区域预报的预报降水处理系统,给出了处理实例.     

基于宽度学习模型的泥石流灾害预报

在泥石流灾害预报模型研究中,科学确定泥石流灾害影响因子及保证模型较高的预报准确率和快速的训练速度是关键问题,也是泥石流灾害预报预警和防灾减灾的重要基础。本研究针对目前泥石流预报模型输入数据维度较大和训练时间较长的问题,采用快速多个主成分并行提取算法(Fast multiple principle components extraction algorithm,FMPCE),选取出6个泥石流灾害影响因子,包括降雨量、山坡坡度、沟床比降、相对高差、土壤含水率和孔隙水压力。基于宽度学习(Broad learning,BL)算法,以泥石流影响因子为输入,泥石流发生概率为输出,构建了泥石流预报模型,并用矩阵随机近似奇异值分解(矩阵随机近似SVD)对模型进行了优化,将优化后宽度学习模型的预报结果与梯度下降法优化的BP神经网络预报模型(GD-BP)、基于支持向量机的预报模型(SVM)、宽度学习预报模型(BL)的结果进行对比,同时,通过输入数据集的扩展,从训练时间上对不同模型进行比较。结果表明,优化宽度学习泥石流灾害预报模型的预报准确率为93.52%,较GD-BP模型、SVM模型和BL模型的预报准确率分别高出1.60%、1.15%和0.03%;优化宽度学习泥石流灾害预报模型的训练时间为0.9039s,较GD-BP模型、SVM模型和BL模型的训练时间分别节省了25.3867 s、17.2620 s和0.8974 s。本研究说明宽度学习算法可以用于对泥石流灾害的发生概率进行预报,同时也可为泥石流预报的实际应用提供新的思路。     

不同时空尺度的泥石流预报技术体系

降水诱发的泥石流是我国主要的泥石流灾害类型。降水是该类泥石流灾害预报的关键动态因子,不同的降水监测和预报技术与方法决定了泥石流预报的不同时空尺度。分析了不同降水监测和预报技术的时空尺度和时空分辨率,并以此为基础,结合下垫面条件分析,建立了不同时空尺度的泥石流预报体系框架:大中区域泥石流预报(数值天气预报)─中小区域泥石流预报(多普勒天气雷达)─单沟泥石流预报(多普勒天气雷达和地面降水遥测)。具体的技术体系包括下垫面条件分析、降水监测和预报方法以及预报结果的表述与发布。     

基于GIS技术的泥石流风险评价研究

为了满足对自然灾害预测不断增长的紧迫要求，泥石流风险评价成为帮助决策过程重要的基础工具之一。即使泥石流风险性各组分的评价很困难，但地理信息系统可辅助提出这种风险性制图的有关方法。我们以云南省为研究区，选取6个成因因子参与泥石流危险度敏感性分析，通过将研究区易损性评价图与危险性评价图叠加分析，编制出云南省泥石流风险评价图。该图描述了在现有自然条件和人类活动下的泥石流风险敏感区。研究成果为全面反映灾情，确定减灾目标，优化防御措施，进行减灾决策提供了重要依据。     

我国自然灾害研究进展与减灾思路调整

我国是世界上自然灾害最为严重的国家之一。为了减轻自然灾害影响和损失,政府、科学家和人民群众进行了大量的减灾研究和实践,在致灾因子、灾害监测预报、灾害评估、自然灾害的社会经济影响、防灾减灾综合化等研究和实践方面均取得了很大进展,减灾思路逐渐从以致灾因子研究和工程预防措施为主调整为全面降低灾害系统脆弱性方面。     

泥石流灾害风险评估理论与方法研究

近年来,频繁发生的泥石流灾害给中国部分地区造成了巨大的破坏。泥石流点多面广、成灾迅速,难以对其进行准确的监测预报,风险评估就显得尤为重要。本文从泥石流灾害风险的构成要素、危险性评估研究和承灾体脆弱程度评估研究等方面分析了泥石流灾害风险的研究现状。从当前的研究现状中可以发现：灾害风险公式得到广大学者普遍认同,泥石流危险性评估方法也相对比较成熟;但在泥石流灾害对承灾体的致损风险机理分析方面研究尚需深入,危险性评估中如何实现从点评价向面评价过渡还需进一步探讨,对承灾体脆弱性研究也需要引起重视。因此,在今后的评估研究中,需要加强这些方面的研究探索,进一步提高泥石流灾害风险评估结果的可信度,提高其实用性。     

山洪泥石流灾害预报预警技术述评

山洪泥石流灾害一直都是山区人民的心头之患，严重威胁着山区人民的生命财产安全。山洪泥石流灾害预报预防，是21世纪我国山丘区防灾减灾的一个重要战略方向。文章通过对国内外大量文献资料的综合分析，评价了目前国内外最具典型的山洪泥石流灾害空间预报技术、时间预报技术以及预警系统开发等预报预警技术，并指出了未来山洪泥石流灾害预报预警技术的发展趋势。     

泥石流滑坡发生的降水预报方法与雨量标准--以四川省盆地区域为例

通过对η坐标数值预报模式预报的降水量检验分析,发现η坐标数值预报模式对青藏高原天气系统活动造成的四川盆地降水预报明显偏弱,且雨区偏北、偏西。我们使用风场资料对高原天气系统作自动识别,进行了对高原天气系统影响降水的强化与雨区漂移的处理,研究得出了η坐标数值预报模式释用强降水预报方法。通过对1991-2001年四川盆地发生泥石流、滑坡灾害的气象成因(强降水)分析,研究得出了四川盆地不同的地质地貌条件下泥石流、滑坡预测雨量标准。在上述研究基础上,建立了四川盆地泥石流、滑坡产生的强降水预报方法。经2003-08-09业务试运行,效果较好,较成功的预报了四川盆地西部、西南部3次大暴雨过程触发的多处泥石流、滑坡灾害,在防灾减灾中发挥了好的作用。     

泥石流防治工程规划的可拓决策技术

泥石流防治工程规划属于一种半结构化的决策问题。可拓决策是根据物元的可拓性，采用定性分析与定量计算相结合的方法解决矛盾问题的一种决策技术。本文根据可拓学的基本理论与方法，提出了一种首先基于系统物元相容性评判进行方案比选；再对初选方案通过系统物元变换进行方案优化设计的可拓决策模型，并以成昆铁路大田箐沟泥石流防治方案的优化设计为例说明这种可拓决策技术的应用程式。     

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.     

西部山区交通线路的泥石流灾害及减灾对策

全面总结了西部山区交通线路的泥石流问题,探讨了交通线路泥石流的特点、分布及其危害现状。在掌握了泥石流特征的基础上,进一步探讨了既有线路、在建线路和拟建线路所采取的不同防灾减灾对策,并提出了山区交通线路泥石流防灾工程建设应该进一步深入研究的问题,为西部山区线路建设中的防灾、减灾工作指明了方向。     

昆明市东川区泥石流信息系统的建立及其应用

根据东川区泥石流的成因、泥石流灾害信息源、各类数据的表达方式及泥石流信息系统的应用等，对东川区泥石流信息系统进行了系统分析，在此基础上利用3S技术，在ARCVIEW的AVENUE开发语言支持下，集成各类数据，建立东川区泥石流信息系统，最后讨论了该系统在泥石流危险度区划及灾害趋势分析中的应用。     

贡嘎山东坡磨西河流域泥石流暴发的临界雨量值初探

在实地调查和前人研究的基础上,查明了贡嘎山东坡磨西河流域内泥石流沟的分布状况、暴发规律及危害特征等;并根据近20年来流域内发生的典型泥石流灾害,分析了泥石流发生前的降雨过程,发现磨西河流域内泥石流发生的当日雨量和前6 d有效雨量之间具有明显的幂函数关系,据此以泥石流发生的当日雨量和前6 d有效雨量为指标,初步探讨了磨西河流域内泥石流暴发的雨量值。     

县级泥石流信息系统的建立及其应用──以四川省丹巴县为例

本文根据软件工程的原理和方法阐述了县级泥石流信息系统的建立过程，并讨论了其应用，主要包括数据库管理系统的应用，用图形库的应用，系统支持下的泥石流危险度分区，泥石流两类分别判别模式的建立。     

区域泥石流孕灾环境危险性评价——以北京军都山区为例

以北京军都山区实测泥石流沟谷数为基准,基于因子叠加、信息量模型和FCM-粗糙集三种方法,分别获得了泥石流灾害发生的危险性等级分布,结果表明:①各分区单位面积内泥石流沟谷数都随着危险性评价等级的提高而增多;②因子叠加法和信息量模型法可得出五级泥石流灾害危险性分级,而粗糙集法只得出三级分级;③以实际泥石流沟谷落在评价区数目为标准,信息量模型法有90%以上的泥石流沟谷在危险性高和极高区域;粗糙集法得到危险区域覆盖了63.72%的泥石流沟谷分布;④从单位面积泥石流沟谷数与泥石流沟谷分布比率可得,信息量模型法评价精度较高,因子叠加法没有形成良好的梯度,而粗糙集法计算等级结果与其他方法存在差异,故须在其他区域进行进一步研究。