| 区域群发性滑坡灾害概率预报系统 |
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| 引用本文： | 李长江,麻土华,朱兴盛,唐小明.区域群发性滑坡灾害概率预报系统[J].国土资源信息化,2005(4):32-38. |
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| 作者姓名： | 李长江 麻土华 朱兴盛 唐小明 |
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| 作者单位： | 浙江省国土资源厅信息中心 杭州310007
(李长江，麻土华，朱兴盛)，浙江省地质矿产研究所 杭州310007(唐小明) |
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| 摘 要： | 根据覆盖浙江全部陆地区域数年降雨量数据,在同时期内有明确日期、位置记录的609处滑坡(泥石流)灾害数据,通过对地质、土壤、土地利用类型、人文等资料的综合分析,研制了集GIS与ANN于一体的区域群发性滑坡灾害概率预报系统(LAPS)。该系统可以通过宽带网络与气象、水文观测等有关部门相连接,在雨季根据雨量观测站的实时观测数据和降雨预报,对全省区域范围内滑坡灾害发生概率进行快速评价,实现对灾害发生的空间范围、强度及其分布概率的实时预警预报,具有维护方便、操作简单、可视化强等特点,可以作为其它滑坡易发地区的预报系统原型。
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| 关 键 词： | 群发性滑坡灾害 概率预报 人工神经网络 GIS |
| 文章编号： | 4481-(2005)04-0032-07 |
| 修稿时间： | 2005年7月16日 |
Probability Prediction System for Regional Group-occurring Landslide Hazards |
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| Li Changjiang,Ma Tuhua,Zhu Xingsheng,Tang Xiaoming Information Center of Zhejiang Provincial Land and Resources.Probability Prediction System for Regional Group-occurring Landslide Hazards[J].Land and Resources Informatization,2005(4):32-38. |
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| Authors: | Li Changjiang Ma Tuhua Zhu Xingsheng Tang Xiaoming Information Center of Zhejiang Provincial Land and Resources |
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| Institution: | Li Changjiang,Ma Tuhua,Zhu Xingsheng,Tang Xiaoming Information Center of Zhejiang Provincial Land and Resources Department,Hangzhou,310007 Zhejiang Institute of Geology and Mineral Resources,Hangzhou,310007 |
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| Abstract: | The probability prediction system of regional group-occurring landslides this paper presents. This system is developed according to the precipitation data covering the total land area of Zhejiang province dusing a pesiod of several years and the recorded data of 609 landslides (debris flow) during the same period, and through comprehensive analysis of geological, soil, land use type and humanity data. The system is based on both GIS and ANN and can be connected via wide band network with the related departments such as meteorological and hydrographic observation centers so as to realize the following functions: to conduct high speed probability prediction of the landslides occurring in the whole area of a province according to the realtime observation data and the rainfall forecast; to conduct early warning and real-time prediction of the spatial scale, intensity and distribution of landslides. The system has characteristics of easy to maintain and operate as well as visualization etc. and can be a model of the prediction system for other landslide-prone regions. |
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| Keywords: | Group-occurring landslides Probability prediction system Artificial neural network Geographical information system |
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