环境演变与土地利用

基于RBFN模型的贵州省石漠化危险度评价

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  • 北京大学资源环境地理系,土地科学中心,北京 100871
黄秋昊 (1977-), 男, 江苏无锡人, 博士研究生, 中国地理学会学生会员, 专业为资源管理与区域开发。 E-mail: qhhuang@pku.edu.cn

收稿日期: 2005-04-06

  修回日期: 2005-05-23

  网络出版日期: 2005-09-25

基金资助

国家自然科学基金重点项目(40335046)

Hazardous Assessment of Karst Rocky Desertification in Guizhou Province: An Application of the RBFN Model

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  • Department of Resources, Environment & Geography, Center for Land Study, Peking University, Beijing 100871, China

Received date: 2005-04-06

  Revised date: 2005-05-23

  Online published: 2005-09-25

Supported by

Key Project of National Natural Science Foundation of China, No.40335046

摘要

西南喀斯特山区石漠化已成为当地发展的重大障碍,国内诸多学者对此做了大量研究工作。本文在前人研究成果的基础上,选取影响喀斯特石漠化演化的相关指标,运用GIS技术提取各指标数据,建立RBFN模型 (Radial Basis Function Network,径向基函数网络模型),根据不同石漠化危险程度标准,选取12个市县相关数据进行训练,确定网络模型参数,进而对贵州省82个市、县 (自治州) 的喀斯特石漠化危险度进行了评价,结果显示:贵州省西南部的西南—东北沿线为喀斯特石漠化发生的极强危险区、整个西南部为强危险区,北部地区为中度危险区,而东南部地区为轻度危险区,并利用他人研究成果进行了对比验证,发现结果较为理想。

本文引用格式

黄秋昊, 蔡运龙 . 基于RBFN模型的贵州省石漠化危险度评价[J]. 地理学报, 2005 , 60(5) : 771 -778 . DOI: 10.11821/xb200505008

Abstract

Karst rocky desertification in karst areas of Southwest China is an important obstacle for the sustainable development of the locality. Many researchers have paid more attention to this issue. Based on the previous research, this paper selects Guizhou province as the research area, quantifies the various indexes influencing the karst rocky desertification, using the GIS technology to get the spatial data, and builds the RBFN (Radial Basis Function Network) model for hazardous assessment. After training the relevant data of the different counties with karst rocky desertification, we get the parameters of the model, and then evaluate the karst rocky desertification. The result shows that the whole southwestern part of Guizhou province is a high hazardous area to karst rocky desertification, the northern part is moderate and the southeastern part, slight. Comparisons with other researches show that the result is in conformity with actual conditions, proving the reasonability and applicability of the RBFN model.

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