云南高原湖泊群的统计学聚类识别及水质响应模式研究
Integrated PCA-BN Approach for Identifying the Water Quality Response Patterns for Lakes in Yunnan Plateau

摘要 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。
Abstract An integrated approach of principle components analysis (PCA) and Bayesian network (BN) for identifying 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 Transparency (SD) and Chl a in both groups.