Application of Support Vector Regression Machines in Soil Moisture Prediction Based on Bacteria Foraging Optimization Algorithm

DING Hui; ZHONG Yue; ZHANG Jun; QIAN Jianzhong; XIE Nenggang

doi:10.13961/j.cnki.stbctb.2016.06.022

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Application of Support Vector Regression Machines in Soil Moisture Prediction Based on Bacteria Foraging Optimization Algorithm

- Application of Support Vector Regression Machines in Soil Moisture Prediction Based on Bacteria Foraging Optimization Algorithm
- Bulletin of Soiland Water Conservation Vol. 36, Issue 6, Pages: 131-135(2016)
- 作者机构：
  
  1. 安徽工业大学机械学院,安徽,马鞍山,243032
  2. 盐城市水利局,江苏,盐城,224005
  3. 盐城市盐都区水务局,江苏,盐城,224005
- 作者简介：
- 基金信息：
- DOI：10.13961/j.cnki.stbctb.2016.06.022
  CLC：
- Published：2016
- 稿件说明：
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DING Hui, ZHONG Yue, ZHANG Jun, et al. Application of Support Vector Regression Machines in Soil Moisture Prediction Based on Bacteria Foraging Optimization Algorithm[J]. Bulletin of Soiland Water Conservation, 2016, 36(6): 131-135.
DOI：

DING Hui, ZHONG Yue, ZHANG Jun, et al. Application of Support Vector Regression Machines in Soil Moisture Prediction Based on Bacteria Foraging Optimization Algorithm[J]. Bulletin of Soiland Water Conservation, 2016, 36(6): 131-135. DOI： 10.13961/j.cnki.stbctb.2016.06.022.

摘要

[目的] 对基于细菌觅食优化算法的支持向量机在土壤墒情预测中的应用进行探讨，为现代农业研究中土壤墒情预测及农业生产提供支持。[方法] 基于支持向量回归机方法建立土壤墒情预测模型，利用细菌觅食优化算法优化支持向量机预测模型的相关参数。根据从种植区采集的田间数据对模型进行建模和测试。[结果] 与仅利用支持向量回归机和利用粒子群优化的支持向量回归机分别建立的模型进行对比，发现本研究所提算法建立的预测模型的预测效果更佳。[结论] 该模型预测效果较好，所建模型已应用于实际项目，预测精度基本满足要求，且运行稳定。进而证明了该研究所提算法的有效性和可行性。

Abstract

[Objective] The application of support vector regression machines in soil moisture prediction based on bacteria foraging optimization algorithm(BFOA) was discussed to provide supports for the prediction of soil moisture of modern agriculture and agricultural production.[Methods] The soil moisture prediction model based on support vector regression machines(SVR) was established.And the related parameters of SVR were optimized by using bacteria foraging optimization algorithm(BFOA).Then the model was set up and tested according to the collected data of growing region.[Results] The proposed algorithm was compared with the established model using SVR and SVR based on particle swarm optimization

respectively. The results showed that the prediction model established by the proposed algorithm performed better.[Conclusion] The model had been applied to the actual project. The prediction accuracy of the model was testified well and the operation was stable. The validity and feasibility of the proposed algorithm had been proved.

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