Application of Support Vector Machines Function Fitting in Groundwater Dynamic Prediction

ZHANG Xia; WANG Ya-ping; LI Zhan-bin; YU Guo-qiang

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Application of Support Vector Machines Function Fitting in Groundwater Dynamic Prediction

- Application of Support Vector Machines Function Fitting in Groundwater Dynamic Prediction
- Bulletin of Soiland Water Conservation Vol. 31, Issue 5, Pages: 277-280(2012)
- 作者机构：
  
  1. 陕西省环境科学研究院,陕西,西安,710061
  2. 西安理工大学西北水资源与环境生态教育部重点实验室,陕西,西安,710048
  3. 中国科学院水利部水土保持研究所黄土高原土壤侵蚀与旱地农业国家重点实验室, 陕西杨凌,712100
  4. 中国地质调查局西安地质矿产研究所,陕西,西安,710054
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2012
- 稿件说明：
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ZHANG Xia, WANG Ya-ping, LI Zhan-bin, et al. Application of Support Vector Machines Function Fitting in Groundwater Dynamic Prediction[J]. Bulletin of Soiland Water Conservation, 2012, 31(5): 277-280.
DOI：

ZHANG Xia, WANG Ya-ping, LI Zhan-bin, et al. Application of Support Vector Machines Function Fitting in Groundwater Dynamic Prediction[J]. Bulletin of Soiland Water Conservation, 2012, 31(5): 277-280. DOI：

摘要

为了深入探求陕西省洛惠渠灌区地下水动态变化情况

在综合分析了各种地下水动态研究方法的基础上

提出了基于支持向量机的灌区地下水动态预测方法。用MATLAB编制了相应的支持向量机程序

建立了地下水动态预测模型。以灌区多年实测数据为学习样本和测试样本

讨论了基于支持向量机的地下水动态分析方法及其可行性。结果表明

支持向量机预测模型具有较高的精度

可以很好地描述地下水动态与各因子之间响应关系

各因子之间相互作用

以及其复杂的耦合关系。研究表明

这种方法运用到灌区地下水动态预测中是切实可行的。

Abstract

To investigate groundwater dynamic in the Luohuiqu irrigation district of Shaanxi Province

different research methods for groundwater dynamic are comprehensively analyzed. The method of support vector machines (SVM) is proposed for predicting groundwater dynamic and building predicting model and two computer programs are compiled using MATLAB. Practical effectiveness of the theory of the SVM for groundwater dynamic prediction is discussed based on the irrigation data measured for many years. Results show that the SVM regression model can well express the complicated coupling relationship of groundwater dynamics. Therefore

the application of this method to the prediction in the irrigation district is feasible and practical.

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Related Author

YU Guo-qiang

LI Zhan-bin

ZHANG Xia

LI Peng

Related Institution

Key Lab of Northwest Water Resources and Environment Ecology of the MOE, XAUT

Research and Design Institute of Environmental Science of Shaanxi Province

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