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• 技术与方法 • 上一篇    

基于人工神经网络的区域水环境承载力评价模型及其应用

王俭1,2;孙铁珩1;李培军1;侯伟2   

  1. 1中国科学院沈阳应用生态研究所, 沈阳 110016;
    2辽宁大学资源与环境学院, 沈阳 110036
  • 收稿日期:2006-06-20 修回日期:2006-10-31 出版日期:2007-01-10 发布日期:2007-01-10

Evaluation model of regional water environment carrying capacity based on artificial neural network and its application in Liaoning Province

WANG Jian1,2; SUN Tie-heng1; LI Pei-jun1; HOU Wei2   

  1. 1Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China;
    2School of Resource and Environment Science, Liaoning University, Shenyang 110036, China
  • Received:2006-06-20 Revised:2006-10-31 Online:2007-01-10 Published:2007-01-10

摘要: 在分析水环境承载力概念及人工神经网络技术基础之上,从阈值角度出发,建立了基于人工神经网络的区域水环境承载力评价模型,并将其应用于辽宁省水环境承载力评价,通过模型计算得到该省水环境承载能力指数。结果表明,2000—2004年,辽宁省水环境承载能力指数分别为0.29、0.36、0.32、0.37和0.43,整体上呈上升趋势,但承载力依然较弱。本文提出的水环境承载力评价模型具有结构简单、建模方便的特点,评价结果可以直观地反映区域水环境承载状态。

关键词: 优化氮肥管理, 土壤硝态氮残留, N表观损失, 粗蛋白含量

Abstract: Based on the concept of water environment carrying capacity and by using artificial neural network, the evaluation model of regional water environment carrying capacity was built from the viewpoint of thresholds, and applied to evaluate the water environment carrying capacity in Liaoning Province. The calculations with the model showed that from 2000 to 2004, the index of water environment carrying capacity in Liaoning Province was 0.29, 0.36, 0.32, 0.37 and 0.43, respectively, suggesting that the water environment carrying capacity in this province was ascending on the whole, but still weak. The model built in this paper had the specialties of simple structure and easily modeling, with which, the regional water environment carrying capacity could be reflected intuitionally.

Key words: Optimized N management, Soil nitrate-N content, Apparent N losses, Crude protein content