Research on the early-warning model with debris flow efficacy coefficient based on the optimal combination weighting law

被引:0
|
作者
机构
[1] [1,Zhang, Yichen
[2] Nie, Lei
[3] Wang, Yanliang
来源
Zhang, Yichen (weifenfangcheng@tom.com) | 1600年 / Springer Science and Business Media Deutschland GmbH卷 / 00期
关键词
Disasters - Disaster prevention;
D O I
10.1007/978-3-642-29107-4_33
中图分类号
X9 [安全科学];
学科分类号
0837 ;
摘要
Early-warning of debris flow plays an important role in disaster mitigation and prevention. Based on optional combination weighting law, the warning model with debris flow efficacy coefficient was built considering the evaluation factors including deposits of loose materials along the gully, gradient of mountain slope, effective precipitation in three dimensions, vegetation coverage rate, relative height difference, watershed area, and total amount of precipitation in a single day. The debris flow which occurred in the city of Dunhua, China was analyzed and evaluated; the data have been used in disaster warning in flood seasons. The result shows that early-warning model with debris flow efficacy coefficient is feasible and practical, and can be regarded as a new method of debris flow warning. © 2013, Springer-Verlag Berlin Heidelberg.
引用
收藏
相关论文
共 50 条
  • [1] A study on the early-warning technique concerning debris flow disasters
    ZHOU Jinxing
    2. College of Resource & Environment
    Journal of Geographical Sciences, 2002, (03) : 115 - 122
  • [2] Research on model of early-warning of enterprise crisis based on entropy
    Tang, Bao-Jun
    Qiu, Wan-Hua
    Sun, Xing
    Kongzhi yu Juece/Control and Decision, 2009, 24 (01): : 113 - 117
  • [3] A framework based on hidden Markov model with adaptive weighting for microcystin forecasting and early-warning
    Jiang, P.
    Liu, X.
    Zhang, J.
    Yuan, X.
    DECISION SUPPORT SYSTEMS, 2016, 84 : 89 - 103
  • [4] Research on Innovation Risk Early-warning Model Based on Bayes Network
    Yang Chao
    Wang Shuang-cheng
    2011 INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND NEURAL COMPUTING (FSNC 2011), VOL I, 2011, : 54 - 57
  • [5] Patent early-warning model based on visualization
    You, Weitao
    Chen, Shi
    Yang, Zhiyuan
    Sun, Zhiqiang
    APPLIED SCIENCE, MATERIALS SCIENCE AND INFORMATION TECHNOLOGIES IN INDUSTRY, 2014, 513-517 : 2722 - 2725
  • [6] Research on the Model of Early-warning of Logistics Outsourcing Risks Based on Four Phases
    Sun, Jiaqing
    Zheng, Han
    2008 4TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND MOBILE COMPUTING, VOLS 1-31, 2008, : 10615 - +
  • [7] Research on early-warning and control model of enterprise finance based on System Dynamics
    Huang, Yang
    PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT, 2011, : 294 - 298
  • [8] Research on early warning of creep landslide by early-warning indictors based on deep displacements
    Chen H.
    Tang H.
    Ge X.
    Li Y.
    Yanshilixue Yu Gongcheng Xuebao/Chinese Journal of Rock Mechanics and Engineering, 2019, 38 : 3015 - 3024
  • [9] Early warning model for slope debris flow initiation
    Li, Ming-li
    Jiang, Yuan-jun
    Yang, Tao
    Huang, Qiang-bing
    Qiao, Jian-ping
    Yang, Zong-ji
    JOURNAL OF MOUNTAIN SCIENCE, 2018, 15 (06) : 1342 - 1353
  • [10] Early warning model for slope debris flow initiation
    Ming-li Li
    Yuan-jun Jiang
    Tao Yang
    Qiang-bing Huang
    Jian-ping Qiao
    Zong-ji Yang
    Journal of Mountain Science, 2018, 15 : 1342 - 1353