Dynamic Clustering Based on Nearest Neighbors for Predicting of Cyanobacteria Bloom in Lakes and Reservoirs

BAI Xiaozhe; ZHANG Huiyan; WANG Xiaoyi; WANG Li; XU Jiping; YU Jiabin

doi:10.13961/j.cnki.stbctb.2017.04.027

您当前的位置：

首页 >

文章列表页 >

Dynamic Clustering Based on Nearest Neighbors for Predicting of Cyanobacteria Bloom in Lakes and Reservoirs

- Dynamic Clustering Based on Nearest Neighbors for Predicting of Cyanobacteria Bloom in Lakes and Reservoirs
- Bulletin of Soiland Water Conservation Vol. 37, Issue 4, Pages: 161-165(2017)
- 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：
- DOI：10.13961/j.cnki.stbctb.2017.04.027
  CLC：
- Published：2017
- 稿件说明：
移动端阅览
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：

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.

关键词

Keywords

references

Sagar R C, Srinivas V V. Effect of DEM source on equivalent Horton-Strahler ratio based GIUH for catchments in two Indian river basins[J]. Journal of Hydrology, 2015,528(1/4):463-489.

O'Donnell G, Nijssen B, Lettenmaier D P. A simple algorithm for generating streamflow networks for grid-based macroscale hydrological models[J]. Hydrological Processes, 1999,13(8):1269-1275.

Garbrecht J, Martz L W. The assignment of drainage direction over flat surfaces in raster digital elevation models[J]. Journal of Hydrology, 1997,193(1/4):204-213.

关颖慧,郑粉莉,王彬,等.基于DEM的黑龙江宾州河流域水系提取试验研究[J].水土保持通报,2012,32(1):127-131.

宋晓猛,张建云,占车生,等.基于DEM的数字流域特征提取研究进展[J].地理科学进展,2013,32(1):31-40.

孙崇亮,王卷乐.基于DEM的水系自动提取与分级研究进展[J].地理科学进展,2008,27(1):118-124.

黄玲,黄金良.基于地表校正和河道烧录方法的河网提取[J].地球信息科学学报,2012,14(2):171-178.

Tribe A. Automated recognition of vally lines and drainage networks from grid digital elevation models:A review and a new method[J]. Journal of Hydrology, 1992,139(1/4):263-293.

Martz W, Garbrecht J. Automated recognition of vally lines and drainage networks from grid digital elevation models:A review and a new method-comment[J]. Journal of Hydrology, 1995,167(1/4):393-396.

Gyasi-Agyei Y, Willgoose G, De Troch F P. Effects of vertical resolution and map scale of digital elevation models on geomorphological parameters used in hydrology[J]. Hydrological Processes, 1995,9(3/4):363-382.

于淼,任立良.基于DEM模型的新填洼算法[J].地球信息科学学报,2009,11(1):50-55.

周德民,程进强,熊立华.基于DEM的洪泛平原湿地数字水系提取研究[J].地理科学,2008,28(6):776-781.

曾红伟,李丽娟,柳玉梅,等. Arc Hydro Tools及多源DEM提取河网与精度分析:以洮儿河流域为例[J].地球信息科学学报,2011,13(1):22-31.

李勇,于宏兵,艾丽娜,张时佳,耿丽娟. Arc Hydro模型提取流域水文信息及精度分析:以松花江流域为例[J].水资源与水工程学报,2013,24(6):120-123.

郑子彦,张万昌,邰庆国.基于DEM与数字化河道提取流域河网的不同方案比较研究[J].资源科学,2009,31(10):1730-1739.

王立,李海强,马放,等.基于SWAT模型的流域河网提取方法[J].中国给水排水,2014,30(13):92-95.

O'Callaghan J F, Mark D M. The extraction of drainage networks from digital elevation data[J]. Computer Vision, Graphics, and Image Processing, 1984,28(3):323-344.

Quinn P, Beven K J, Chevallier P, et al. The prediction of hillslope flow paths for distributed hydrological modeling using digital terrain models[J]. Hydrological Processes, 1991,5(1):59-79.

David M. ArcGIS Hydro Data Model[M]. USA:The University of Texas at Austin:Center for Research in Water Resources, 2001:321-330.

朱思蓉,吴华意. Arc Hydro水文数据模型[J].测绘与空间地理信息,2006,29(5):87-90.

Arnod J G, Srinivasan R, Muttiah R S, et al. Continental scale simulation of the hydrologic balance[J]. Journal of American Water Resources Association, 1999,35(5):1037-1051.

詹蕾. SRTM DEM的精度评价及其适用性研究[D].江苏南京:南京师范大学,2008.

唐从国,刘丛强.基于SRTM DEM数据的清水江流域地表水文模拟[J].辽宁工程技术大学学报:自然科学版,2009,28(4):652-655.

Views

1122

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Additional Soil Erosion Predication of Natural Gas Pipe Project from West to East in Shaanxi Province

Intelligent Evaluation of Loess Collapsibility

Research on Predicting Index of Phosphorus Leaching from Soil

A Study on Prediction of Boundary Angle of Surface Movement by BP Artificial Neural Network

Water Environment Evaluation and Pollution Control in Industrial Park of Cangxi County

Related Author

WANG Cun-rong

ZHANG Jian

KOU Quan

FU Liang-yong

LI Yingliang

SONG Xueqing

CUI Zhongxing

WU Yanqing

Related Institution

Xifeng Water and Soil Conservation Station of Huanghe River Conservancy Committee, Xifeng

Electronic Comprehensive Investigation and Surveying Institute of Ministry of Information Industry

Xi’an Design and Research Institute of Coal Industry

Xi’an University of Technology

College of Resources and Environmental Science, Northwest University of Agriculture and Forestry, Yangling

⁰