利用BP神经网络方法预测西湖叶绿素a的浓度

Applications of back propagation neural network for predicting the concentration of chlorophyll-a in West Lake

在西湖共设了 8个采样点 ,通过主成分分析选取了最能代表西湖水质状况的 7号点 (湖心 )作为研究对象。根据 2 0 0 0年 1月至 2 0 0 1年 4月西湖常规监测的水生生态数据 ,并用插值的方法使其生成足够多的样本数 ,利用 BP人工神经网络 ,探索其用于西湖水生生态状况 (叶绿素 a的浓度 )的短期变化趋势预测的可行性 ,从中找出最能反映西湖水生生态状况变化趋势的水质因子用来建立网络。并用 3号点的数据来检验网络的泛化性能 ,发现网络输出值与实际值吻合度较高。结果表明 ,水温和叶绿素a对未来一周的叶绿素 a含量影响最大 ,以这两者作为输入变量建立的网络简单、快捷 ,比其他线性数值模拟预测有较大的优势。说明人工神经网络对叶绿素 a的预测是一种有效工具 ,可为西湖富营养化治理提供科学依据。

We have established 8 sampling spots in West Lake, and selected spot 7 which can most represent the water quality status of it as study object by principal component analysis. With sufficient samples got by the inserted method, based on the aquatic data (2000.1～2001.4) of West Lake by routine measurement, we studied the feasibility of using Back propagation (BP) neural network to predict the short term trends of the state of aquatic ecology (the concentration of chlorophyll-a), and looked for the most influential elements which can reflect the trends of aquatic ecology in West Lake for modeling. At the same time, we used the data of spot 3 to test the universality of the network, and found the outputs tallied with the measured values very well. The results show that water temperature and chlorophyll-a affect the concentration of chlorophyll-a of next week most greatly. The network using them as input variables is simple and prompt, having greater advantage than other linearity numeric modeling. This indicates that artificial neural network is an effective method for forecasting the concentration of chlorophyll-a. And it can provide the scientific basis for the control of the eutrophication of West Lake.