基于SVR模型的重庆市生态安全指标预测模型研究

计算机科学 ›› 2013, Vol. 40 ›› Issue (8): 245-248.

基于SVR模型的重庆市生态安全指标预测模型研究

冯海亮,夏磊,黄鸿

重庆大学光电技术及系统教育部重点实验室重庆400044;重庆大学光电技术及系统教育部重点实验室重庆400044;重庆大学光电技术及系统教育部重点实验室重庆400044

出版日期:2018-11-16 发布日期:2018-11-16
基金资助:
本文受重庆市科技攻关重点项目(CSTC2009AB2231)资助

Study on Prediction Model of Ecological Security Index in Chongqing City Based on SVR Model

FENG Hai-liang,XIA Lei and HUANG Hong

Online:2018-11-16 Published:2018-11-16

摘要/Abstract

摘要： 鉴于实际工作中生态安全指标数据统计的滞后性,通过采集并整理重庆市1988年至2007年与生态安全指数极度相关的28项指标构建了重庆市生态安全指标体系,并以此作为训练样本数据,以2008、2009年对应指标作为输出结果,分析比较多变量灰色预测模型、径向基神经网络、支持向量回归方法,通过对比真实统计数据做出误差分析,得到支持向量回归重庆市生态安全指标预测模型。该模型对重庆市主要生态安全指标进行预测的结果具有明显的优势,可以用于实际预测。

关键词: 生态安全预测,多变量灰色模型,径向基神经网络,支持向量回归

Abstract: In actual practice,owing to hysteresis of the conventional statistical analysis on ecological safety index,this article implemented Multivariable Grey model,Radical Basis Function Network and Support Vector Regression to input extremely relevant samples of ecological safety index from 1998to 2007in Chongqing．The outputs generated by the three models were evaluated and compared with ecological safety index gathered in 2008and 2009．According to the error analysis between the outputs and the actual index,more accurate predictions were produced by the Support Vector Regression model．In conclusion,the Support Vector Regression model is applicable to actual practice and has higher accuracy than the other two models.

Key words: Ecological prediction,Multivariable grey model,Radical basis function,Support vector regression

冯海亮,夏磊,黄鸿. 基于SVR模型的重庆市生态安全指标预测模型研究[J]. 计算机科学, 2013, 40(8): 245-248. https://doi.org/

FENG Hai-liang,XIA Lei and HUANG Hong. Study on Prediction Model of Ecological Security Index in Chongqing City Based on SVR Model[J]. Computer Science, 2013, 40(8): 245-248. https://doi.org/

参考文献

[1] 黄鸿,杨媚,冯海亮,等．PSR模型支持下重庆市主城区生态安全评价[J].微计算机信息,2012,8:1-3
[2] Vapnik V N．The nature of statistical learning theory [M]．New York:Springer,1995:12-38
[3] 熊和金,徐华中.灰色控制 [M].北京:国防工业出版社,2005:47-50
[4] Nekoukar,Beheshti H,Taghi M．A local linear radial basis function neural network for financial time-series forecasting[J]．2010,3(3):352-356
[5] Piggio T,Girosi F．Theory of networks for approximation and learning[R]．A I Merno No 1140．Artificial Intelligence Laboratory,Massachusetts Institute of Technology,Cambridge Mass,1989
[6] Liu Hong,Cui Wen-hua,Zhang Qing-ling．Nonlinear combination forecasting model and application based on radial basis function neural networks[C]∥Control,Automation and System Engineering,2009． CASE 2009．IITA Internatonal Conference.2009:387-390
[7] Beatson R K,Levesley J,Mouat C T．Better bases for radial basis function interpolation problems[J]．Journal of Computational and Applied Mathematics,2011,6(4):434-446
[8] Pai Ping-feng ,Kuo-Ping Lin,Chi-Shen Lin．Time series forecasting by a seasonal support vector regression model[J]．Expert Systems with Applications,2010,7(6):4261-4265
[9] AI-Anazi A F,Gates I D．Support vector regression for porosity prediction in a heterogeneous reservoir:A comparative study [J]．Computers & Geosciences,2010,6(12):1494-1503
[10] Stoean R．Evolutionary support vector regression machines[C]∥SYNASC’06Proceedings of the Eighth International Symbolic and Numeric Algorithms for Scientific Computing．2006:330-335
[11] Alenezi,Abdulrahman．Real-time prediction of order flowtimes using support vector regression [J]．Computers & Operations Research,2008,5(11):3489-3503
[12] 重庆市统计局,国家统计局重庆调查总队.重庆统计年鉴[M].北京:中国统计出版社,1988-2009
[13] Lan Yu-qing,Guo Shu-hang．Multiple stepwise regression analysis on knowledge evaluation [C]∥2008International Conferen-ce on Management of e-Commerce and e-Government,2008

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed