云南高原湖泊群的统计学聚类识别 及水质响应模式研究

为探究湖泊群水质变量的响应模式,构建适用于监测数据匮乏的湖泊群聚类和响应模式识别方法体系(PCA-BN),包括4个步骤:数据预处理、PCA降维与湖泊聚类、贝叶斯网络构建及参数学习、湖泊响应关系模拟。以云南高原湖泊群为例开展研究,结果表明:所研究的26个湖泊可分为两类;由于第一类湖泊受到的人为干扰更严重,因而叶绿素a对总氮和总磷的响应比第二类湖泊更敏感;第一类湖泊表层水温高,溶解氧趋近饱和,随叶绿素a变化不显著,第二类湖泊溶解氧随叶绿素a升高而显著升高;两类湖泊的透明度与叶绿素a的关系一致。

Integrated PCA-BN Approach for Identifying the Water Quality Response Patterns for Lakes in Yunnan Plateau

An integrated approach of principle components analysis (PCA) and Bayesian network (BN) for identify-ing the response pattern of different clusters were developed to understand sensitive relationships of water quality in lakes of Yunnan Plateau. The model includes four steps: data preconditioning, lakes clustering with PCA, Bayesian network learning and lake water quality response modeling. The results demonstrate that the 26 lakes can be clustered into two groups; the Chl a concentration responds more significantly to Total Nitrogen (TN) and Total Phosphorus (TP) in the first group, mainly resulting from much higher watershed disturbances; the Dissolved Oxygen (DO) in the first group with higher water temperature is close to saturation and have little change with Chl a increasing, while the second group is not; and there is good consistency on the relationship between Transpa-rency (SD)and Chl a in both groups.