文章摘要
李娜,范海梅,许鹏,叶属峰.BP神经网络模型在象山港水环境承载力研究中的应用[J].上海海洋大学学报,2019,28(1):125-133
BP神经网络模型在象山港水环境承载力研究中的应用
Application of BP neural network model in water environmental carrying capacity research of Xiangshan Bay
投稿时间:2017-08-12  修订日期:2018-09-12
DOI:10.12024/jsou.20170802116
中文关键词: BP神经网络  水环境承载力  象山港  指标阈值
英文关键词: BP neural network  water environmental carrying capacity  Xiangshan Bay  index threshold
基金项目:国家海洋局海洋公益性行业科研专项(201505008)
作者单位
李娜 上海海洋大学 海洋生态与环境学院, 上海 201306 
范海梅 国家海洋局东海环境监测中心, 上海 201206 
许鹏 国家海洋局东海环境监测中心, 上海 201206 
叶属峰 国家海洋局东海环境监测中心, 上海 201206 
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中文摘要:
      为研究近年象山港水环境承载力状况,根据2010-2013年象山港水质指标DO、COD、DIN和DIP的统计数据获取指标阈值,应用BP神经网络技术建立象山港水环境承载力研究模型。模型输入指标为DO、COD、DIN和DIP的监测值,输出为水环境承载力指数。应用构建的模型对2014年春、夏、秋、冬象山港水环境承载力进行研究,结果表明:2014年象山港水环境承载力指数季节平均值都小于0.4,水环境承载力总体不理想。象山港湾内的水环境承载力整体高于外海。湾口受外海影响,水环境承载力常年偏低;内湾水环境承载力季节变化复杂,主要为局部影响;湾中部水环境承载力春季偏高,夏季偏低,这与生物活动有关。BP神经网络模型结构简单、数据结果直观可靠,可应用于象山港水环境承载力问题的研究。
英文摘要:
      In order to study the water environmental carrying capacity (WECC) of Xiangshan Bay in recent years, the thresholds of the water quality parameters DO, COD, DIN and DIP were obtained according to the statistics of Xiangshan Bay from 2010 to 2013. Then BP neural network technology was applied to establish a WECC model of Xiangshan Bay. The input of the model are the monitoring data of DO, COD, DIN and DIP. The output of the model was the Water Environmental Carrying Capacity Index (WECCI). The model was applied in the study of the WECC of Xiangshan Bay in the four seasons of 2014. The results show that:the seasonal-averaged WECCI of Xiangshan Bay in 2014 is always bellow 0.4, so the WECC of Xiangshan Bay is not ideal; The WECC of Xiangshan Bay is higher in the inshore than offshore area; The WECC is low all through the year in the bay mouth, influenced by the offshore water; The seasonal variation of WECC is complicated in the inner bay, and it is mainly locally influenced; The WECC in the central bay is high in spring, and low in summer, which is influenced by biological activity; The structure of BP neural network is simple, and the results are intuitive and reliable. Therefore, BP neural network could be used in the study of the WECC of Xiangshan Bay.
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