动态聚类最近邻法在湖库蓝藻水华预测中的应用

白晓哲; 张慧妍; 王小艺; 王立; 许继平; 于家斌

doi:10.13961/j.cnki.stbctb.2017.04.027

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动态聚类最近邻法在湖库蓝藻水华预测中的应用

试验研究

- 动态聚类最近邻法在湖库蓝藻水华预测中的应用
- Dynamic Clustering Based on Nearest Neighbors for Predicting of Cyanobacteria Bloom in Lakes and Reservoirs
- 水土保持通报 2017年37卷第4期页码：161-165
- 作者机构：
  
  北京工商大学计算机与信息工程学院食品安全大数据技术北京市重点实验室,北京,100048
- 作者简介：
- 基金信息：
  
  北京市属高校创新能力提升计划项目“北京河湖水环境监测与智能管理物联网应用平台”（PXM2014_014213_000033）;北京市教委科技重点项目（KZ201510011011）;北京市属高校青年拔尖人才培育计划（CIT&TCD201404031）
- DOI：10.13961/j.cnki.stbctb.2017.04.027
  中图分类号：
- 纸质出版：2017
- 稿件说明：
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白晓哲, 张慧妍, 王小艺, 等. 动态聚类最近邻法在湖库蓝藻水华预测中的应用[J]. 水土保持通报, 2017,37(4):161-165.

BAI Xiaozhe, ZHANG Huiyan, WANG Xiaoyi, et al. Dynamic Clustering Based on Nearest Neighbors for Predicting of Cyanobacteria Bloom in Lakes and Reservoirs[J]. Bulletin of Soiland Water Conservation, 2017, 37(4): 161-165.
白晓哲, 张慧妍, 王小艺, 等. 动态聚类最近邻法在湖库蓝藻水华预测中的应用[J]. 水土保持通报, 2017,37(4):161-165. DOI： 10.13961/j.cnki.stbctb.2017.04.027.

BAI Xiaozhe, ZHANG Huiyan, WANG Xiaoyi, et al. Dynamic Clustering Based on Nearest Neighbors for Predicting of Cyanobacteria Bloom in Lakes and Reservoirs[J]. Bulletin of Soiland Water Conservation, 2017, 37(4): 161-165. DOI： 10.13961/j.cnki.stbctb.2017.04.027.

摘要

[目的]探索湖库蓝藻水华的有效预测方法，为水环境污染防治关键问题的解决提供科学依据。[方法]结合蓝藻水华演化中表现出的混沌类随机特点，提出一种基于有效性函数优化的动态聚类算法，以实现蓝藻水华动态、小范围近邻优化预测的目的。首先，基于动态聚类算法对监测数据进行典型类的客观划分，为后续有效减小搜索空间，提高预测精度奠定基础；而后采用粒子群算法优化得到各类的最佳近邻个数，以确定参与回归建模的观测值数量；最后依据最近邻观测数据建立动态回归预测模型。[结果]采用太湖金墅监测站点2011年叶绿素a浓度测定值进行建模，之后对2012年叶绿素a浓度进行短期预测。新建模型的预测值与实际值运行趋势一致，且相对误差为12.02%，而基于传统聚类线性回归算法的相对误差为15.21%，基于BP神经网络预测算法的相对误差为19.51%，相空间重构算法的相对误差为38.42%。[结论]算例结果表明该方法的预测精度相对较高，证明了所提优化预测方法的可行性与有效性。

Abstract

[Objective] It is one of the key basic issue in the prevention and control of water environment by exploring effective prediction methods about cyanobacteria bloom in lakes and reservoirs.[Methods] Combined with the class random characteristic showed in the chaotic evolution of cyanobacteria bloom

this paper proposed a dynamic clustering algorithm based on the optimization of validity functions to achieve the optimal cluster number of cyanobacteria bloom and small-scale neighborhood optimal prediction. First of all

monitoring data were classified objectively by the proposed dynamic clustering algorithm to reduce effectively the search space and to improve the prediction accuracy. Then the optimal number of neighbors for all kinds was obtained using the particle swarm optimization algorithm

which was used to determine the number of participating in the local regressive algorithm. Finally

a dynamic regressive prediction model was established.[Results] The model established using the concentration data of chlorophyll a at the Jinshu monitoring site of Taihu Lake in 2011 was used to model and predict short-term variation of it in 2012. The predicted value of the model was consistent with the actual trend and the relative error was 12.02%

and was smaller than the ones predicted by other models

such as linear regression algorithm based on traditional clustering

BP neural network

and phase space reconstruction algorithm

whose relative errors were 15.21%

19.51% and 38.42%.[Conclusion] Numerical results showed that the prediction accuracy of this method was relatively high

hence the feasibility and effectiveness of the optimization prediction method proposed were proved.

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