基于模糊预测对象的证据权方法及其在土地沙漠化评价中的应用

张生元; 武强; 成秋明; 葛咏

基于模糊预测对象的证据权方法及其在土地沙漠化评价中的应用

张生元^{1, 2, 3,},
武强¹,
成秋明^{3, 4},
葛咏^{3, 5}

1.
中国矿业大学资源与安全工程学院，北京 100083
2.
石家庄经济学院经贸学院，河北石家庄 050031
3.
中国地质大学地质过程与矿产资源国家重点实验室，湖北武汉 430074
4.
加拿大约克大学地球与空间科学工程系，多伦多 M3J1P3
5.
中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室，北京 100101

基金项目:

地质过程和矿产资源国家重点实验室开放基金项目 MGMR200209

地质过程和矿产资源国家重点实验室开放基金项目 GPMR0503

详细信息

作者简介:
张生元(1961—), 男, 副教授, 主要从事统计和地理信息系统开发和应用研究. E mail: zhangsh3002@126.com

中图分类号: P628
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出版历程
- 收稿日期: 2005-08-12
- 刊出日期: 2006-05-25

Weights of Evidence Method Based on Fuzzy Training Layer and Its Application in Desertification Assessment

ZHANG Sheng-yuan^{1, 2, 3
,},
WU Qiang¹,
CHENG Qiu-ming^{3, 4},
GE Yong^{3, 5}

1.
School of Resource and Safty Engineering, China University of Mining & Technology, Beijing 100083, China
2.
School of Economics and Commerce, Shijiazhuang University of Economics, Shijiazhuang 050031, China
3.
State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wulmn 430074, China
4.
Department of Earth and Space Science and Engineering, York University, Toronto M3J1P3, Canada
5.
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

摘要

摘要: 为了使在地理信息系统中被广泛用于点事件预测的证据权方法能对面事件进行评价和预测, 提出了一种新的基于模糊训练层的证据权方法.它是一种更广泛的证据权方法, 与普通证据权方法所不同的是, 它的训练层是模糊集合, 其取值是它的隶属度.通过适当的变换也可以把点训练层转换为模糊集合.因此, 该方法可以对面事件、点事件和线事件进行评价和预测.该方法可以处理训练层和证据层均为模糊集合的情况, 被称为双重模糊证据权方法.作为该方法的一个应用实例, 本文介绍毛乌素沙漠边缘的晋陕蒙地区土地沙漠化评价的应用实例.
- 证据权方法 /
- 面事件 /
- 点事件 /
- 模糊预测对象 /
- 沙漠化评价 /
- 训练层
Abstract: This paper proposes an extended version of the fuzzy weights of evidence method, based on the fuzzy training layer for assessing and predicting the occurrence of area-events. It can be considered as a generalization of the ordinary weights of evidence method, which is used to predict the occurrence of point-events with known evidences and training layers. Unlike the ordinary weights of evidence using point training sets, the new method involves training data as a fuzzy set. Point training data can be converted to a fuzzy set, therefore the new method can predict both point-events and area-events. When both evidence and training data are fuzzy sets, the new method acts as a dual fuzzy weights of evidence method. A case study has been used to demonstrate the application of the method in assessing desertification in the area in the contact of Shanxi and Shaanxi Provinces and Inner Mongolia Autonomous Region.
- weights of evidence method /
- area-event /
- point-event /
- fuzzy set /
- fuzzy object /
- desertification

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图 1 7个证据层和作为训练层的土地沙漠化隶属度

a.矿山利用面积(km²); b.地貌类型; c.土地利用类型; d.母质类型; e.植被覆盖度; f.年平均降水量(mm/a); g.年风暴次数; h.训练层的沙漠化隶属度

Fig. 1. Seven evidence layers and the fuzzy membership function of training layer

下载: 全尺寸图片幻灯片

图 2 应用基于模糊预测对象的证据权方法得到的沙漠化后验概率

Fig. 2. Posterior probability obtained by using fuzzy weights of evidence model based on fuzzy training layer

下载: 全尺寸图片幻灯片

表 1 用GeoDAS GIS得到的植被覆盖度的统计结果

Table 1. Statistics of the degree of vegetation coverage calculated by means of GeoDAS GIS

表 2 各个证据层的隶属度及其模糊证据权

Table 2. Fuzzy membership function, fuzzy weights and stand- ard deviation for each evidence

参考文献(14)

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