洪湖位于江汉平原，是湖北省最大湖泊，水域面积344 km2，集调蓄洪水、供水、灌溉、航运、生物保护、旅游、养殖等功能于一体。洪湖湿地保护区已经被列入国家自然保护区名录和中国重要湿地名录，但由于流域污染严重，湖泊养殖在2005年前泛滥，导致湖泊水质恶化，特别是高锰酸盐指数常年处于Ⅴ类状态，丰水期处于Ⅳ类状态，远未达到自然保护区的地表Ⅱ类水的水质要求，因此洪湖的水环境管理和水污染防治工作任重道远。本文以中国科学院知识创新工程重大项目“长江中下游地区湖泊富营养化的发生机制与控制对策研究”为契机，以洪湖为研究对象，对洪湖的富营养化评价，建立预测模型，并提出应对富营养化的防治措施，为洪湖水环境管理提供参考依据。

本文对洪湖的研究时段主要集中在2005年8月至2006年7月，根据洪湖的水体利用方式和自然特征，将洪湖划分为河流入湖区、养殖区、湿地保护区、开阔水域四个区，各区设置若干监测点。通过对这些点的周年监测获得本文研究数据。利用模型数学评价法对洪湖的富营养化评价，结果表明，河流入湖区与养殖区轻度富营养化，开阔水域、湿地保护区中营养。此外，本文还根据国家环保部的《湖泊（水库）富营养化评价方法及分级技术规定》，利用综合营养状态指数法对洪湖的营养状态作了评价，除了养殖区的评价结果比模糊数学方法评价的结果低一个营养级外，其他结果都一致。结合洪湖水质监测结果可以看出：模糊数学评价法的评价结果比综合营养状态指数法的结果与实际情况更吻合。

明确湖泊富营养化的主导因子能为湖泊富营养化的治理提供有效的理论指导。一般地，湖泊的富营养化主要受氮、磷两种营养元素的影响，本文用因子分析法，在SPSS14.0环境下，对洪湖的八项水质监测指标作了因子分析。洪湖富营养化的主要影响因子是CODMn、NH3-N、TP、SD、 pH和叶绿素a，TN没有进入主要影响因子的行列，主要是因为洪湖的氮磷质量比远大于10，在这种情况下，磷元素成为浮游植物的限制因子。

本文为洪湖建立了BP人工神经网络富营养化预测模型，模型采用单隐含层式网络结构，十三个隐含层节点，输入层有十个输入节点，输出层有六个输出节点。模型除了考虑营养盐和水环境物理指标对水质的影响外，还将风力和水期纳入到模型的输入单元。模型训练和验证结果较好，网络训练的实测值与拟合值的标准偏差为0.0185，相关系数显著水平R=0.998；模型验证的预测结果的相对误差较小，相对误差在20%以内的占总数的88.89%；相对误差大于40.%的仅占总数的5.56%。

结合洪湖的实际情况，文章为洪湖水环境管理建立了水质预警机制，水质预警分为零级、轻微级、中度级、重度级和严重级五个等级，并对富营养化的控制提出了对策和建议。

Honghu lake is the biggest lake of Hubei Province which located in Jianghan Plain, and it has many functions such as storing floodwater, providing water, irrigation, shipping, biology protection, tour and aquiculture. Honghu wetland protection refuge has been enlisted as state reserve and China important wetland directory. However, drainage pollution and excessive aquiculture make the water worse. Especially, the CODMn is perennially in V state and in Ⅳ state in water plentiful period, which don’t meet the standards of the water quality of Nature Reserve. The water environment management and the water pollution control work in Honghu Lake have a long way to go. This paper is based on the CAS Knowledge Innovation Project of major projects "Middle and lower reaches of the Yangtze River areas of eutrophication of lakes and control mechanism Countermeasures". In this paper, Honghu Lake as the studied area, the author have evaluated the trophic level,  established a foresting model, and have offered some control measures to deal with eutrophication which will provide reference for environmental management.

In this paper the research object is Honghu Lake’s water environment from August 2005 to July 2006. The lake was divided into aquiculture region, inlet region, open water column and wetland protection refuge according to the lake’s using and nature characteristic. The author used Fuzzy Mathematics Method to evaluate the trophic levels of Honghu Lake and the evaluation outcome shows that the inlet region and aquiculture region middle eutrophic; open water column, wetland protection refuge Mesotrophic.

It could provide an effective referencing and decision-making for lake management if dominant trophic factors of the lake are definitized. Generally, the trophication of lake is mainly impacted by nitrogen nutrients and phosphorus nutrients.  The author analyzed eight factors of Honghu Lake using Factor Analytical Method in SPSS14.0. The analysis result shows that CODMn, NH3-N, TP, SD, pH and chlorophyll a are the main  influencing factors. Because the TN/TP ratio is more than 10:1, TN isn’t the main influencing factor of Honghu lake’s eutrophication.

A BP Artificial Neural Networks of eutrophication forecasting model for Honghu Lake was established which contains single-hidden layer, 13 hidden layer nerve cells, 10 input nerve cells and 6 output nerve cells. The model adds lake’s windvane and hydro-level physical indicators to the input nerve cells. The precision of training and validating is good: the standard deviation between measure value and fitting value is 0.0185; the correlation coefficient significant level (R) is 0.998; the relative error of Model validating is small. The number of relative error which is less than 20 % adds up to 88.89 % of the total.

In this paper the author founded a water quality forewarning mechanism containing zero grade, slight grade, moderate grade, important grade and serious grade for Honghu Lake, and offered some advices and countmeasures for lake eutrophication