院报 ›› 2012, Vol. 29 ›› Issue (7): 21-26.DOI: 10.3969/j.issn.1001-5485.2012.07.005

• 水资源与环境 • 上一篇    下一篇

汕头市水资源承载力评价研究

陈凯1, 李就好1, 李永刚2, 屈寒飞1   

  1. 1.华南农业大学 水利与土木工程学院，广州510642；2.广东省机械技师学院，广州510450
  • 收稿日期:2011-10-27 出版日期:2012-07-01 发布日期:2012-07-25
  • 通讯作者: 李就好（1965-），男，江西万年人，教授，博导，主要从事农业水土方面的研究
  • 作者简介:陈凯（1974-），男，吉林长春人，博士研究生，主要从事农业水土方面研究
  • 基金资助:

    广东省水利科技创新与推广项目（小水电与生态环境关系的研究2009-20）

Evaluation of Water Resources Carrying Capacity in Shantou City

CHEN Kai1, LI Jiu-hao1, LI Yong-gang2, QU Han-fei1   

  1. 1.College of Water Conservancy and Civil Engineering, South China Agricultural University, Guangzhou510642， China;  2.Guangdong Machinery Technician College, Guangzhou510450, China
  • Received:2011-10-27 Online:2012-07-01 Published:2012-07-25

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摘要： 在对汕头市水资源状况及社会经济发展状况调查的基础上，结合水资源承载力评价基本原理，选取了14个适合汕头市的水资源承载力评价指标，并将承载力等级划分为3级，再分别采用RBF神经网络模型、BP神经网络模型和模糊层次综合评价模型3种方法对汕头市2000—2009年水资源承载力状况进行评价。结果表明：汕头市水资源承载力等级总体介于Ⅰ级与П级之间，情况较好，但有逐步恶化趋势；RBF神经网络模型法与模糊层次综合评价法比BP神经网络模型法评价效果好，适用性更强。

关键词: 水资源承载力, 神经网络模型, RBF, BP, 模糊综合评价

Abstract: Based on investigation of water resources and local economic and social development in Shantou City, we performed research on the carrying capacity of water resources in this area. We selected fourteen evaluation indexes suitable for Shantou, and classified the evaluation grades into 3 levels. RBF neural network model, BP neural network model and fuzzy comprehensive evaluation model were employed respectively to evaluate the carrying capacity of water resources from 2000 to 2009. Results show that the carrying capacity in Shantou is between grade I and II, which is in a good overall situation but tends to deteriorating gradually. The evaluation effect and applicability of RBF neural network model and fuzzy comprehensive evaluation model are better than those of BP neural network model. 

Key words: water resources carrying capacity, artificial neural network model, RBF, BP, fuzzy comprehensive evaluation

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