华东师范大学学报(自然科学版)

• 地理学 • 上一篇    下一篇

上海市水资源生态足迹分析与SVR预测

曹 坤, 刘素霞   

  1. 华东师范大学 计算机科学技术系, 上海 200241
  • 收稿日期:2015-09-15 出版日期:2016-07-25 发布日期:2016-09-29
  • 通讯作者: 刘素霞, 女, 副教授, 硕士生导师, 研究方向为计算机应用.E-mail: sxliu@cs.ecnu.edu.cn.

Analysis and SVR predication of water resources ecological footprint in Shanghai

CAO Kun, LIU Su-xia   

  1. Department of Computer Science and Technology, East China Normal University, Shanghai 200241, China
  • Received:2015-09-15 Online:2016-07-25 Published:2016-09-29

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摘要：

根据水资源生态足迹模型, 以北京市2001---2013年水资源生态足迹和生态承载力为参照, 对上海市同年份的水资源生态足迹和生态承载力进行计算并对比分析了利用现状；引入机器学习中的支持向量回归机 SVR, 对上海市 2014---2016 年生态足迹进行了预测, 并给出可能的影响因素. 研究结果表明, 2001---2010 年上海市历年水生态足迹基本持平且数值较大, 2011---2013 年较往年出现明显下降. 2001---2013 年一直表现为生态赤字, 水资源差值生态压力指数年均值为 -3.083 11, 说明本地的水资源无法自给自足, 需要过境水的补给来满足生产生活的需求. 上海市万元GDP和万元工业GDP生态足迹呈下降趋势, 说明产业结构在优化, 水资源经济效益在提高, 但其年均值都高于北京市, 仍存在改善空间. 预测2014---2016年生态足迹总体呈上升趋势, 水危机形势日益严峻, 保护水资源, 促进可持续发展已刻不容缓.

关键词: 水生态足迹, 支持向量回归机, 可持续利用;上海市

Abstract:

Based on the ecological footprint model and the carrying capacities of water resources from 2001 to 2013 in Beijing, the ecological footprint and the carrying capacities in Shanghai at the same year were calculated and the current situation was analyzed. Furthermore, using the techniques of Support Vector Regression (SVR) in machine learning, the ecological footprints in Shanghai from 2014 to 2016 were predicted, and the possible influencing factors were discussed. The results show that the ecological footprints of water from 2001 to 2010 keep steady, while from 2011 to 2013 there are significant reductions
compared with previous years. The status of water resources from 2001 to 2013 have been characterized by ecological deficit, and the annual average of difference of water resources ecological pressure index is −3.083 11, which demonstrates that the local water resources cannot be self-sufficient, so as to need transit water resources to meet the needs of normal production and living. In spite of the industrial structure in the optimization and economic benefits of water resources in improving, the ecological footprint of ten thousand yuan GDP and ten thousand yuan industrial GDP in Shanghai is on the decline, and the average
is higher than Beijing, and therefore there is still improve space. The predicted results show that the ecological footprint from 2014 to 2016 will present a rising trend every year. The situation of water crisis will be increasingly serious, thus measures of protection of water resources and promoting the sustainable development should be put into effect.

Key words: water ecological footprint, Support Vector Regression, sustainable development, Shanghai