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

机译:泥石流灾害预警技术研究

摘要

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.
机译:根据泥石流爆发的原理,提出了新的洪流分类技术。利用该技术,洪流可分为破坏力高的泥石流洪流,泥石流洪流,高载砂量洪流洪流和普通洪流洪流四种类型。本文提出了分类指标体系和定量评价方法。基于洪流分类,确定了泥石流洪灾危险区映射技术,可以准确确定泥石流灾害预警对象。本文详细介绍了建立泥石流灾害神经网络实时预报模型的关键技术,包括确定输入层,输出层和隐含层的神经节点,知识源的构建。以及初始重量值等等。利用该技术,根据历史泥石流灾害的降雨特征,建立了泥石流灾害实时预报神经网络模型,该模型包括多个降雨因素,如灾害发生日的降雨,灾害发生前15天的降雨等。一天,一小时十分钟的最大降雨量。它可以通过实时降雨监测或天气预报来预测泥石流爆发的概率,临界降雨。基于洪流分类和危害区图,结合雨季降雨监测和实时预报模型,建立泥石流灾害预警系统。该系统采用GIS技术,国际先进的软件和硬件,使系统性能稳定,扩展性好。该系统是一个可视化信息系统,用于管理和决策,可以帮助及时检查洪流类型和危险区域的变化,洪流管理,灾害预警和减灾与预防。

著录项

  • 来源
    《地理学报(英文版)》 |2002年第3期|363-370|共8页
  • 作者

  • 作者单位

    Inst.of Forestry Research, Chinese Academy of Forestry Science, Beijing 100091, China;

    College of Resource & Environment, Beijing Forestry University, Beijing 100083, China;

    College of Resource & Environment, Beijing Forestry University, Beijing 100083, China;

    Inst.of Forestry Research, Chinese Academy of Forestry Science, Beijing 100091, China;

    College of Resource & Environment, Beijing Forestry University, Beijing 100083, China;

  • 收录信息 中国科学引文数据库(CSCD);
  • 原文格式 PDF
  • 正文语种 chi
  • 中图分类 泥石流;
  • 关键词

    debris flows; disaster early-warning technique; torrent classification; mapping of the hazard zones; the neural networks technique;

    机译:泥石流;灾害预警技术;洪流分类;危险区映射;神经网络技术;

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