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基于复杂网络的城市湖库藻类水华形成识别研究
引用本文:邵飞,施彦,王小艺,许继平,王立,盛璐,唐丽娜.基于复杂网络的城市湖库藻类水华形成识别研究[J].环境科学学报,2014,34(8):2121-2125.
作者姓名:邵飞  施彦  王小艺  许继平  王立  盛璐  唐丽娜
作者单位:北京工商大学 计算机与信息工程学院, 北京 100048;北京工商大学 计算机与信息工程学院, 北京 100048;北京工商大学 计算机与信息工程学院, 北京 100048;北京工商大学 计算机与信息工程学院, 北京 100048;北京工商大学 计算机与信息工程学院, 北京 100048;北京工商大学 计算机与信息工程学院, 北京 100048;北京工商大学 计算机与信息工程学院, 北京 100048
基金项目:国家自然科学基金(No.51179002);北京市市属高校科研能力提升计划项目(No.PXM2013_014213_000098);北京市教委科技创新平台项目(No.PXM2013_014213_000044);北京市教委科技计划面上项目(No.KM201110011006)
摘    要:在对城市湖库藻类水华形成机理深入研究的基础上,将提取的影响水华暴发的关键因子总磷(TP)、总氮(TN)、温度(T)、pH值、溶解氧(DO)、光照(I)、叶绿素a浓度(chl_a)作为网络节点,将影响因素间的关系抽象成网络的边,构建了藻类水华形成的有向网络模型.同时,计算了复杂网络的统计特征参数,构建了节点的关键度模型,并进行修正,进而构建了水华形成的复杂网络统计特征参数模型G,对其进行半定量分级,从而实现对水华暴发的有效识别,最后采用北京城市河湖水质的实验数据对模型进行了验证.结果表明,复杂网络统计特征参数G与叶绿素a浓度有显著的相关性,能够较好地表征水华形成过程.

关 键 词:复杂网络  藻类水华  形成机理  水华识别
收稿时间:2013/10/14 0:00:00
修稿时间:2/4/2014 12:00:00 AM

Recognition of lake algal bloom based on complex network
SHAO Fei,SHI Yan,WANG Xiaoyi,XU Jiping,WANG Li,SHENG Lu and TANG Lina.Recognition of lake algal bloom based on complex network[J].Acta Scientiae Circumstantiae,2014,34(8):2121-2125.
Authors:SHAO Fei  SHI Yan  WANG Xiaoyi  XU Jiping  WANG Li  SHENG Lu and TANG Lina
Institution:Beijing Technology and Business University, College of Computer and Information Engineering, Beijing 100048;Beijing Technology and Business University, College of Computer and Information Engineering, Beijing 100048;Beijing Technology and Business University, College of Computer and Information Engineering, Beijing 100048;Beijing Technology and Business University, College of Computer and Information Engineering, Beijing 100048;Beijing Technology and Business University, College of Computer and Information Engineering, Beijing 100048;Beijing Technology and Business University, College of Computer and Information Engineering, Beijing 100048;Beijing Technology and Business University, College of Computer and Information Engineering, Beijing 100048
Abstract:Based on the deep analysis of the formation mechanism of algal bloom in urban lakes, a directed graph model is built. In this complex network, several key factors including total phosphorus (TP), total nitrogen (TN), temperature (T), pH, dissolved oxygen (DO), illumination (I) and chl_a are extracted to be the nodes of the network, and the affective relationship between the key factors are abstracted to be the side of the network. Furthermore, the statistic characteristic parameters of this complex network are calculated and a critical model of the node is developed and corrected. Then a numerical model is built for the statistical characteristic parameter G of algal bloom. Through semi-quantitative grading of G, the valid recognition of algal bloom is achieved. Finally, this method is used and validated for water quality monitoring data of urban lake in Beijing. The result suggests that numerical model G for complex network is strongly associated with the density of chl_a, which can better present the process of bloom formation.
Keywords:complex network  algal bloom  formation mechanism  bloom recognition
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