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基于SOFM神经网络的京津冀地区水源涵养功能分区
引用本文:刘 娅,朱文博,韩 雅,李双成. 基于SOFM神经网络的京津冀地区水源涵养功能分区[J]. 环境科学研究, 2015, 28(3): 369-376
作者姓名:刘 娅  朱文博  韩 雅  李双成
作者单位:1.北京大学城市与环境学院, 地表过程分析与模拟教育部重点实验室, 北京 100871
基金项目:国家自然科学基金项目(41371096,41130534)
摘    要:为探讨水源涵养功能空间分异在水资源分区管理方面的应用,以京津冀地区为研究区,根据水源涵养功能的5个主要影响要素——海拔、降水量、蒸散量、土壤饱和含水率、森林覆盖度,构建SOFM(self-organizing feature map,自组织特征映射)神经网络,并对该区水源涵养功能进行区域划分. 结果表明:京津冀地区可划分为6个水源涵养功能区域,分别为冀西北高原-间山盆地草原水源涵养中能力区、坝上高原-冀北山地草原-森林水源涵养中低能力区、燕山-太行山中低山森林-草原水源涵养中高能力区、冀中南部平原农田水源涵养低能力区、燕山-太行山低山森林水源涵养中高能力区和冀东平原农田-草原水源涵养高能力区. 方差分析结果显示,各分区之间具有显著差异,表明用SOFM神经网络对水源涵养功能进行分区效果良好. 在水源涵养功能分区基础上,归纳和总结了各分区水源涵养功能影响要素的主要特征. 京津冀地区作为一个统一的水资源生态环境区,其内部的水源涵养功能存在明显的区域差异,建议依据各分区水源涵养功能的强弱及其主要控制因子特征,科学制订适应当地自然环境的水资源管理方案. 

关 键 词:水源涵养   SOFM神经网络   区域划分   京津冀地区

Regionalization of Water Conservation Function of Beijing-Tianjin-Hebei Area Based on SOFM Neural Network
LIU Ya , ZHU Wenbo , HAN Ya , LI Shuangcheng. Regionalization of Water Conservation Function of Beijing-Tianjin-Hebei Area Based on SOFM Neural Network[J]. Research of Environmental Sciences, 2015, 28(3): 369-376
Authors:LIU Ya    ZHU Wenbo    HAN Ya    LI Shuangcheng
Affiliation:1.Key Laboratory for Earth Surface Processes of Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China2.Key Laboratory for Earth Surface Processes of Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China ;Key Laboratory for Environmental and Urban Sciences, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
Abstract:Abstract:Water conservation function is one of the most important ecosystem services. Identification of functional areas as well as classification are significant for water management. A self-organizing feature map (SOFM) was built to identify the different functional areas of water conservation, considering five important factors in the Beijing-Tianjin-Hebei Area. The results showed that the best choice would be to divide Beijing-Tianjin-Hebei Area into six major functional areas of water conservation. Each area showed a comparatively clear and complete boundary. Taking characteristics of topography and vegetation cover of the whole area into account, the six functional regions were named as Northwest Hebei Plateau and mountain basin medium capacity area, Bashang Plateau and north Hebei mountain medium-low capacity area, Yan and Taihang medium-low mountains medium-high capacity area, central and southern Hebei Plain low capacity area, Yan and Taihang low mountains medium-high capacity area, and Eastern Hebei Plain high capacity area. The ANOVA test showed that significant differences existed among the six regions, proving good behavior of SOFM in regionalizing water conservation function. Based on the division, the present study summarized the main features of each sub-district and made recommendations for the conservation and utilization of water in this region, combined with ecological zoning proposals. Conspicuous spatial variation of water conservation function existed within Beijing-Tianjin-Hebei Area, which was seen as an unified ecological zone. Thus the strength of water conservation function in each sub region and its controlling factors need to be considered when making the water management plan specifically for local natural environment.
Keywords:water conservation  SOFM neural network  regionalization  Beijing-Tianjin-Hebei Area
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