熵值理论和改进BP网络在湖泊富营养化评价中的应用研究  

Study on Lake Eutrophication Based on Entropy Theory and Improved BP Neural Network

作　　者：于凤存[1] 方国华[2,3] 鞠琴[3] 

机构地区：[1]安徽省.水利部淮河水利委员会科学研究院(水利水资源安徽省重点实验室),安徽蚌埠233000 [2]河海大学水利水电工程学院,江苏南京210098 [3]河海大学水文水资源与水利工程科学国家重点实验室,江苏南京210098

出　　处：《水文》2014年第3期57-60,共4页Journal of China Hydrology

基　　金：国家自然科学基金(41105077)

摘　　要：针对湖泊型饮用水源地水体污染、富营养化加剧的问题,引入熵值理论,建立单指标营养状态指数(TSI)和熵权藕合的湖泊综合营养状态指数模型(STSI),计算得到湖泊综合富营养状态指数判断湖泊富营养综合状态;基于神经网络仿真理论和Matlab软件系统,采用附加动量法和自适应学习速率改进BP算法,建立5-3-1结构型式的BP网络模型对湖泊富营养状态进行仿真预测。综合富营养化指数模型及改进BP模型应用于评价及预测固城湖富营养状态,并对模型评价结果进行验证。结果表明,改进BP网络模型可以有效地综合判断水体状态,为富营养评价及预测提供新的方法。Aiming at the water quality pollution and euttophication problems for drinking water source of lake type, the synthesized throphic state index （STSI） model was established based on throphic state index （TSI） and entropy weights to evaluating lake eutrophication status through calculating STSI. Based on neural network simulation theory and Matlab software, BP algorithm model was improved through additional momentum method and the learning rate self-adjustment. Improved BP model of 5-3-1 type was established to simulate and predict lake eutrophication. The synthesized throphic state index （STSI） model and improved BP model were applied to Gucheng Lake to evaluate and predict throphic state, and the model results were verified. The results show that the improved BP model can determine the water body＇s state, which provides a new method to evaluate and predict eutrophication.

关 键 词：湖泊富营养化 营养状态指数 熵权 仿真系统 神经网络 BP算法 

分 类 号：X524[环境科学与工程—环境工程]