数据驱动的模糊支持向量农业水质评价模型

张慧妍; 段瑜; 王小艺; 许继平; 郑蕾

doi:10.13961/j.cnki.stbctb.2019.01.023

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数据驱动的模糊支持向量农业水质评价模型

试验研究

- 数据驱动的模糊支持向量农业水质评价模型
- Data-Driven Fuzzy Support Vector Model for Agriculture Water Quality Evaluation
- 水土保持通报 2019年39卷第1期页码：142-146
- 作者机构：
  
  1. 北京工商大学食品安全大数据技术北京市重点实验室,北京,100048
  2. 北京师范大学水科学研究院,北京,100875
- 作者简介：
- 基金信息：
  
  国家自然科学基金项目"时空大数据驱动的蓝藻水华预测预警方法研究"（61703008）;北京市教委科技计划重点项目（KZ201510011011）;科技创新服务能力建设（PXM2018_014213_000033）
- DOI：10.13961/j.cnki.stbctb.2019.01.023
  中图分类号：
- 纸质出版：2019
- 稿件说明：
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张慧妍, 段瑜, 王小艺, 等. 数据驱动的模糊支持向量农业水质评价模型[J]. 水土保持通报, 2019,39(1):142-146.

Zhang Huiyan, Duan Yu, Wang Xiaoyi, et al. Data-Driven Fuzzy Support Vector Model for Agriculture Water Quality Evaluation[J]. Bulletin of Soiland Water Conservation, 2019, 39(1): 142-146.
张慧妍, 段瑜, 王小艺, 等. 数据驱动的模糊支持向量农业水质评价模型[J]. 水土保持通报, 2019,39(1):142-146. DOI： 10.13961/j.cnki.stbctb.2019.01.023.

Zhang Huiyan, Duan Yu, Wang Xiaoyi, et al. Data-Driven Fuzzy Support Vector Model for Agriculture Water Quality Evaluation[J]. Bulletin of Soiland Water Conservation, 2019, 39(1): 142-146. DOI： 10.13961/j.cnki.stbctb.2019.01.023.

摘要

[目的]针对在线农业水质综合评价中的监测数据噪声及边界模糊问题，建立具有良好抗扰性和等级划分的综合评价模型。[方法]提出了基于数据确定投影寻踪指标权重及模糊隶属度参数的支持向量评价模型。采用改进遗传算法对投影寻踪函数进行了优化求解，获得相对客观的指标权重向量，而后结合数据优化模糊隶属度参数，构建模糊支持向量综合评价模型，以使得监测噪声对评价模型泛化能力的影响减小。此外，考虑到通用的离散化评价等级分辨率较低，提出了区域划分信度的概念，用以辅助说明样本所属区域划分等级的可信程度，实现对综合评价结果进行细化补充说明的目的。[结果]评价模型与专家意见及传统评价方法的结果吻合程度较高，且在监测数据叠加10%至30%的随机噪声时，模型仍能保持85%以上的一致率，样本的区域划分可信度均大于临界值，抗扰效果优于传统模糊综合评价及灰色聚类法。[结论]本文构建的模型具有较好的可行性与鲁棒性，能为后续噪声存在条件下农业水质在线实时综合评价提供借鉴与参考。

Abstract

[Objective] We aimed to solve the problem of monitoring data noise and boundary ambiguity in comprehensive evaluation of agricultural water quality

in order to establish a comprehensive evaluation model with good disturbance resistance and grade division. [Methods] A data-driven fuzzy support vector evaluation method was proposed to determine index weight of projection pursuit index and the parameters of fuzzy membership. Improved genetic algorithm was adapted to optimize the projection pursuit function and obtain the relatively objective index weigh. Then the parameters of fuzzy membership were optimized with data

and a comprehensive evaluation model of fuzzy support vector machine was constructed to reduce the influence of monitoring noise on the generalization ability of the evaluation model. In addition

considering the low resolution of the general discrete evaluation grade

the concept of regional division reliability was proposed to explain the reliability of the regional division grade of the sample

to further explain the comprehensive evaluation results. [Results] The model evaluation results were consistent with the results from experts and traditional evaluation. The model maintained more than 85% consistent rate with the monitoring data with 10%～30% random noise

and the reliability of regional division of samples was greater than the critical value

indicating the reliability and robustness of the method. The results from the constructed model were better than the fuzzy comprehensive evaluation and grey clustering method. [Conclusion] The method proposed by the present study is feasible and robust

and it can provide a reference for real-time evaluation of agricultural water quality under the condition of subsequent noise.

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references

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