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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

刘 娅 朱文博 韩 雅 李双成

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.
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.
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.
Citation: 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.

基于SOFM神经网络的京津冀地区水源涵养功能分区

基金项目: 国家自然科学基金项目(41371096,41130534)

Regionalization of Water Conservation Function of Beijing-Tianjin-Hebei Area Based on SOFM Neural Network

  • 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.

     

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