CAJ | 학술논문

以星云湖为研究对象，通过多年水生态监测数据筛选出富营养化的关键因子，利用BP神经网络模拟叶绿素a与各因子之间的关系，定量分析了叶绿素a的压力响应情况，结果表明：CODMn、TP、TN是富营养化进程中3个关键因子；以0.02 mg/L为富营养化湖泊中叶绿素a的控制目标，需分别削减61％的CODMn或77％的TP或20％的TN．模拟结果显示，星云湖的藻类生长以氮为限制因子．基于神经网络模拟分析星云湖的富营养化进程，为星云湖水污染控制提供重要的决策依据．
이성운호위연구대상，통과다년수생태감측수거사선출부영양화적관건인자，이용BP신경망락모의협록소a여각인자지간적관계，정량분석료협록소a적압력향응정황，결과표명：CODMn、TP、TN시부영양화진정중3개관건인자；이0.02 mg/L위부영양화호박중협록소a적공제목표，수분별삭감61％적CODMn혹77％적TP혹20％적TN．모의결과현시，성운호적조류생장이담위한제인자．기우신경망락모의분석성운호적부영양화진정，위성운호수오염공제제공중요적결책의거．
Lake Xingyun was selected as a study object .The key factors of the eutrophication were screened out using PCA, and back-propagate neural network was used to simulate the relation between chlorophyll a and key fac -tors, and the pressure-response effect between chlorophyll a and key factors was quantitatively analyzed .The con-clusions are:CODMn , TP and TN were the key factors of the eutrophication .Set 0.02 mg/L as the control target of chlorophyll a, then 61%of CODMn or 77% of TP or 20%of TN should be reduced .This result indicated that N was the limiting factor of the phytoplankton in Lake Xingyun .This simulation of eutrophication provided the basic data for the remediation of Lake Xingyun .