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中文摘要：
水体环境包含多个影响因素,因素间大多具有非线性相关性,为了能够客观地对大沽河湿地海水水质进行综合评价,以神经网络为基础,利用溶解氧、化学需氧量、无机氮、活性磷酸盐、石油类5个指标作为评价因子,建立了5×5×1拓扑结构的BP人工神经网络模型,通过该模型对大沽河湿地水质进行综合评价。同时采用单因子评价方法、内梅罗指数法对该海域环境状况进行评价,以期更好的对比评价BP人工神经网络模型的优缺点。BP人工神经网络模型评价结果显示,大沽河河道内站点的水质均为劣Ⅳ类水质,入海河流断面及其周边海域也达到了Ⅲ类及以上水质标准。调查海域无机氮含量超标严重,劣Ⅳ类及以上站位的数量占总调查站位的59.3%,富营养化状态明显。通过单因子评价法、内梅罗指数法、BP人工神经网络3种评价方法对大沽河湿地水质进行评价,发现Ⅲ类及以上水质站位占总调查站位比例分别为89%、96%、56%。与单因子评价法、内梅罗指数法相比,BP人工神经网络模型设计合理、评价结果科学可靠,是一种更加快捷、客观全面及实用的水体质量评价方法。
英文摘要：
Most water environmental factors have a nonlinear correlation. To assess sea water quality, the Back Propagation （BP） artificial neural network model was established based on the concept and principle of artificial neural network. Taking DO, COD, DIN, PO43- and petroleum as the evaluation factors, the BP neural network was established to evaluate the water quality for Dagu River wetland. The results indicated that the sea water quality of Dagu River was worse than the classⅣ water quality standard, and the water quality of its surrounding area were or exceeded the class III water quality standard. The survey sea area was mainly affected by inorganic nitrogen and phosphate. The study found that the number of inorganic nitrogen content that exceededⅣ levels accounted for 59.3% of all respondents, and that eutrophication was obvious. These results indicated that BP neural network method was reasonable in design and higher in generalization compared with single factor evaluation and Nemerow pollution index, and that it is an objective, effective and practical environmental quality evaluation method. Thus, BP artificial neural network was a better level of fast, handy and valid ability to evaluate the sea water quality.
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